from re import sub
from bs4 import BeautifulSoup
from nltk import download, PorterStemmer
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from requests import get, Response
from rich import printdownload("stopwords")
download("wordnet")
download("punkt_tab")[nltk_data] Downloading package stopwords to /usr/share/nltk_data...
[nltk_data] Package stopwords is already up-to-date!
[nltk_data] Downloading package wordnet to /usr/share/nltk_data...
[nltk_data] Package wordnet is already up-to-date!
[nltk_data] Downloading package punkt_tab to /usr/share/nltk_data...
[nltk_data] Package punkt_tab is already up-to-date!Trueresponse: Response = get(
"https://en.wikipedia.org/wiki/Natural_language_processing", timeout=5
)
HTML: str = response.textsoup: BeautifulSoup = BeautifulSoup(HTML, "html.parser")
paragraph: str = soup.get_text()print(paragraph)
Natural language processing - Wikipedia
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1
History
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1.1
Symbolic NLP (1950s – early 1990s)
1.2
Statistical NLP (1990s–present)
2
Approaches: Symbolic, statistical, neural networks
Toggle Approaches: Symbolic, statistical, neural networks subsection
2.1
Statistical approach
2.2
Neural networks
3
Common NLP tasks
Toggle Common NLP tasks subsection
3.1
Text and speech processing
3.2
Morphological analysis
3.3
Syntactic analysis
3.4
Lexical semantics (of individual words in context)
3.5
Relational semantics (semantics of individual sentences)
3.6
Discourse (semantics beyond individual sentences)
3.7
Higher-level NLP applications
4
General tendencies and (possible) future directions
Toggle General tendencies and (possible) future directions subsection
4.1
Cognition
5
See also
6
References
7
Further reading
8
External links
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Natural language processing
71 languages
AfrikaansالعربيةԱրեւմտահայերէնAzərbaycancaবাংলা閩南語 / Bân-lâm-gíБеларускаяБеларуская
(тарашкевіца)БългарскиBosanskiBrezhonegCatalàČeštinaCymraegDanskDeutschEestiΕλληνικάEspañolEsperantoEuskaraفارسیFra
nçaisGaeilgeGalego한국어Հայերենहिन्दीHrvatskiIdoBahasa
IndonesiaIsiZuluÍslenskaItalianoעבריתಕನ್ನಡქართულიLatviešuLietuviųМакедонскиमराठीمصرىМонголမြန်မာဘာသာNederlands日本語Norsk
bokmålଓଡ଼ିଆپښتوPicardPiemontèisPolskiPortuguêsQaraqalpaqshaRomânăRuna SimiРусскийShqipSimple EnglishکوردیСрпски /
srpskiSrpskohrvatski / српскохрватскиSuomiதமிழ்తెలుగుไทยTürkçeУкраїнськаTiếng Việt粵語中文
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From Wikipedia, the free encyclopedia
Processing of natural language by a computer
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Natural language processing (NLP) is the processing of natural language information by a computer. The study of
NLP, a subfield of computer science, is generally associated with artificial intelligence. NLP is related to
information retrieval, knowledge representation, computational linguistics, and more broadly with linguistics.[1]
Major processing tasks in an NLP system include: speech recognition, text classification, natural language
understanding, and natural language generation.
History
Further information: History of natural language processing
Natural language processing has its roots in the 1950s.[2] Already in 1950, Alan Turing published an article titled
"Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of
intelligence, though at the time that was not articulated as a problem separate from artificial intelligence. The
proposed test includes a task that involves the automated interpretation and generation of natural language.
Symbolic NLP (1950s – early 1990s)
The premise of symbolic NLP is well-summarized by John Searle's Chinese room experiment: Given a collection of
rules (e.g., a Chinese phrasebook, with questions and matching answers), the computer emulates natural language
understanding (or other NLP tasks) by applying those rules to the data it confronts.
1950s: The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences
into English. The authors claimed that within three or five years, machine translation would be a solved
problem.[3] However, real progress was much slower, and after the ALPAC report in 1966, which found that ten years
of research had failed to fulfill the expectations, funding for machine translation was dramatically reduced.
Little further research in machine translation was conducted in America (though some research continued elsewhere,
such as Japan and Europe[4]) until the late 1980s when the first statistical machine translation systems were
developed.
1960s: Some notably successful natural language processing systems developed in the 1960s were SHRDLU, a natural
language system working in restricted "blocks worlds" with restricted vocabularies, and ELIZA, a simulation of a
Rogerian psychotherapist, written by Joseph Weizenbaum between 1964 and 1966. Using almost no information about
human thought or emotion, ELIZA sometimes provided a startlingly human-like interaction. When the "patient"
exceeded the very small knowledge base, ELIZA might provide a generic response, for example, responding to "My head
hurts" with "Why do you say your head hurts?". Ross Quillian's successful work on natural language was demonstrated
with a vocabulary of only twenty words, because that was all that would fit in a computer memory at the time.[5]
1970s: During the 1970s, many programmers began to write "conceptual ontologies", which structured real-world
information into computer-understandable data. Examples are MARGIE (Schank, 1975), SAM (Cullingford, 1978), PAM
(Wilensky, 1978), TaleSpin (Meehan, 1976), QUALM (Lehnert, 1977), Politics (Carbonell, 1979), and Plot Units
(Lehnert 1981). During this time, the first chatterbots were written (e.g., PARRY).
1980s: The 1980s and early 1990s mark the heyday of symbolic methods in NLP. Focus areas of the time included
research on rule-based parsing (e.g., the development of HPSG as a computational operationalization of generative
grammar), morphology (e.g., two-level morphology[6]), semantics (e.g., Lesk algorithm), reference (e.g., within
Centering Theory[7]) and other areas of natural language understanding (e.g., in the Rhetorical Structure Theory).
Other lines of research were continued, e.g., the development of chatterbots with Racter and Jabberwacky. An
important development (that eventually led to the statistical turn in the 1990s) was the rising importance of
quantitative evaluation in this period.[8]
Statistical NLP (1990s–present)
Up until the 1980s, most natural language processing systems were based on complex sets of hand-written rules.
Starting in the late 1980s, however, there was a revolution in natural language processing with the introduction of
machine learning algorithms for language processing. This was due to both the steady increase in computational
power (see Moore's law) and the gradual lessening of the dominance of Chomskyan theories of linguistics (e.g.
transformational grammar), whose theoretical underpinnings discouraged the sort of corpus linguistics that
underlies the machine-learning approach to language processing.[9]
1990s: Many of the notable early successes in statistical methods in NLP occurred in the field of machine
translation, due especially to work at IBM Research, such as IBM alignment models. These systems were able to take
advantage of existing multilingual textual corpora that had been produced by the Parliament of Canada and the
European Union as a result of laws calling for the translation of all governmental proceedings into all official
languages of the corresponding systems of government. However, most other systems depended on corpora specifically
developed for the tasks implemented by these systems, which was (and often continues to be) a major limitation in
the success of these systems. As a result, a great deal of research has gone into methods of more effectively
learning from limited amounts of data.
2000s: With the growth of the web, increasing amounts of raw (unannotated) language data have become available
since the mid-1990s. Research has thus increasingly focused on unsupervised and semi-supervised learning
algorithms. Such algorithms can learn from data that has not been hand-annotated with the desired answers or using
a combination of annotated and non-annotated data. Generally, this task is much more difficult than supervised
learning, and typically produces less accurate results for a given amount of input data. However, there is an
enormous amount of non-annotated data available (including, among other things, the entire content of the World
Wide Web), which can often make up for the worse efficiency if the algorithm used has a low enough time complexity
to be practical.
2003: word n-gram model, at the time the best statistical algorithm, is outperformed by a multi-layer perceptron
(with a single hidden layer and context length of several words, trained on up to 14 million words, by Bengio et
al.)[10]
2010: Tomáš Mikolov (then a PhD student at Brno University of Technology) with co-authors applied a simple
recurrent neural network with a single hidden layer to language modelling,[11] and in the following years he went
on to develop Word2vec. In the 2010s, representation learning and deep neural network-style (featuring many hidden
layers) machine learning methods became widespread in natural language processing. That popularity was due partly
to a flurry of results showing that such techniques[12][13] can achieve state-of-the-art results in many natural
language tasks, e.g., in language modeling[14] and parsing.[15][16] This is increasingly important in medicine and
healthcare, where NLP helps analyze notes and text in electronic health records that would otherwise be
inaccessible for study when seeking to improve care[17] or protect patient privacy.[18]
Approaches: Symbolic, statistical, neural networks
Symbolic approach, i.e., the hand-coding of a set of rules for manipulating symbols, coupled with a dictionary
lookup, was historically the first approach used both by AI in general and by NLP in particular:[19][20] such as by
writing grammars or devising heuristic rules for stemming.
Machine learning approaches, which include both statistical and neural networks, on the other hand, have many
advantages over the symbolic approach:
both statistical and neural networks methods can focus more on the most common cases extracted from a corpus of
texts, whereas the rule-based approach needs to provide rules for both rare cases and common ones equally.
language models, produced by either statistical or neural networks methods, are more robust to both unfamiliar
(e.g. containing words or structures that have not been seen before) and erroneous input (e.g. with misspelled
words or words accidentally omitted) in comparison to the rule-based systems, which are also more costly to
produce.
the larger such a (probabilistic) language model is, the more accurate it becomes, in contrast to rule-based
systems that can gain accuracy only by increasing the amount and complexity of the rules leading to intractability
problems.
Rule-based systems are commonly used:
when the amount of training data is insufficient to successfully apply machine learning methods, e.g., for the
machine translation of low-resource languages such as provided by the Apertium system,
for preprocessing in NLP pipelines, e.g., tokenization, or
for postprocessing and transforming the output of NLP pipelines, e.g., for knowledge extraction from syntactic
parses.
Statistical approach
In the late 1980s and mid-1990s, the statistical approach ended a period of AI winter, which was caused by the
inefficiencies of the rule-based approaches.[21][22]
The earliest decision trees, producing systems of hard if–then rules, were still very similar to the old rule-based
approaches.
Only the introduction of hidden Markov models, applied to part-of-speech tagging, announced the end of the old
rule-based approach.
Neural networks
Further information: Artificial neural network
A major drawback of statistical methods is that they require elaborate feature engineering. Since 2015,[23] the
statistical approach has been replaced by the neural networks approach, using semantic networks[24] and word
embeddings to capture semantic properties of words.
Intermediate tasks (e.g., part-of-speech tagging and dependency parsing) are not needed anymore.
Neural machine translation, based on then-newly invented sequence-to-sequence transformations, made obsolete the
intermediate steps, such as word alignment, previously necessary for statistical machine translation.
Common NLP tasks
The following is a list of some of the most commonly researched tasks in natural language processing. Some of these
tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in
solving larger tasks.
Though natural language processing tasks are closely intertwined, they can be subdivided into categories for
convenience. A coarse division is given below.
Text and speech processing
Optical character recognition (OCR)
Given an image representing printed text, determine the corresponding text.
Speech recognition
Given a sound clip of a person or people speaking, determine the textual representation of the speech. This is the
opposite of text to speech and is one of the extremely difficult problems colloquially termed "AI-complete" (see
above). In natural speech there are hardly any pauses between successive words, and thus speech segmentation is a
necessary subtask of speech recognition (see below). In most spoken languages, the sounds representing successive
letters blend into each other in a process termed coarticulation, so the conversion of the analog signal to
discrete characters can be a very difficult process. Also, given that words in the same language are spoken by
people with different accents, the speech recognition software must be able to recognize the wide variety of input
as being identical to each other in terms of its textual equivalent.
Speech segmentation
Given a sound clip of a person or people speaking, separate it into words. A subtask of speech recognition and
typically grouped with it.
Text-to-speech
Given a text, transform those units and produce a spoken representation. Text-to-speech can be used to aid the
visually impaired.[25]
Word segmentation (Tokenization)
Tokenization is a process used in text analysis that divides text into individual words or word fragments. This
technique results in two key components: a word index and tokenized text. The word index is a list that maps unique
words to specific numerical identifiers, and the tokenized text replaces each word with its corresponding numerical
token. These numerical tokens are then used in various deep learning methods.[26]
For a language like English, this is fairly trivial, since words are usually separated by spaces. However, some
written languages like Chinese, Japanese and Thai do not mark word boundaries in such a fashion, and in those
languages text segmentation is a significant task requiring knowledge of the vocabulary and morphology of words in
the language. Sometimes this process is also used in cases like bag of words (BOW) creation in data mining.
Morphological analysis
Lemmatization
The task of removing inflectional endings only and to return the base dictionary form of a word which is also known
as a lemma. Lemmatization is another technique for reducing words to their normalized form. But in this case, the
transformation actually uses a dictionary to map words to their actual form.[27]
Morphological segmentation
Separate words into individual morphemes and identify the class of the morphemes. The difficulty of this task
depends greatly on the complexity of the morphology (i.e., the structure of words) of the language being
considered. English has fairly simple morphology, especially inflectional morphology, and thus it is often possible
to ignore this task entirely and simply model all possible forms of a word (e.g., "open, opens, opened, opening")
as separate words. In languages such as Turkish or Meitei, a highly agglutinated Indian language, however, such an
approach is not possible, as each dictionary entry has thousands of possible word forms.[28]
Part-of-speech tagging
Given a sentence, determine the part of speech (POS) for each word. Many words, especially common ones, can serve
as multiple parts of speech. For example, "book" can be a noun ("the book on the table") or verb ("to book a
flight"); "set" can be a noun, verb or adjective; and "out" can be any of at least five different parts of speech.
Stemming
The process of reducing inflected (or sometimes derived) words to a base form (e.g., "close" will be the root for
"closed", "closing", "close", "closer" etc.). Stemming yields similar results as lemmatization, but does so on
grounds of rules, not a dictionary.
Syntactic analysis
Part of a series onFormal languages
Key concepts
Formal system
Alphabet
Syntax
Formal semantics
Semantics (programming languages)
Formal grammar
Formation rule
Well-formed formula
Automata theory
Regular expression
Production
Ground expression
Atomic formula
Applications
Formal methods
Propositional calculus
Predicate logic
Mathematical notation
Natural language processing
Programming language theory
Mathematical linguistics
Computational linguistics
Syntax analysis
Formal verification
Automated theorem proving
vte
Grammar induction[29]
Generate a formal grammar that describes a language's syntax.
Sentence breaking (also known as "sentence boundary disambiguation")
Given a chunk of text, find the sentence boundaries. Sentence boundaries are often marked by periods or other
punctuation marks, but these same characters can serve other purposes (e.g., marking abbreviations).
Parsing
Determine the parse tree (grammatical analysis) of a given sentence. The grammar for natural languages is ambiguous
and typical sentences have multiple possible analyses: perhaps surprisingly, for a typical sentence there may be
thousands of potential parses (most of which will seem completely nonsensical to a human). There are two primary
types of parsing: dependency parsing and constituency parsing. Dependency parsing focuses on the relationships
between words in a sentence (marking things like primary objects and predicates), whereas constituency parsing
focuses on building out the parse tree using a probabilistic context-free grammar (PCFG) (see also stochastic
grammar).
Lexical semantics (of individual words in context)
Lexical semantics
What is the computational meaning of individual words in context?
Distributional semantics
How can we learn semantic representations from data?
Named entity recognition (NER)
Given a stream of text, determine which items in the text map to proper names, such as people or places, and what
the type of each such name is (e.g. person, location, organization). Although capitalization can aid in recognizing
named entities in languages such as English, this information cannot aid in determining the type of named entity,
and in any case, is often inaccurate or insufficient. For example, the first letter of a sentence is also
capitalized, and named entities often span several words, only some of which are capitalized. Furthermore, many
other languages in non-Western scripts (e.g. Chinese or Arabic) do not have any capitalization at all, and even
languages with capitalization may not consistently use it to distinguish names. For example, German capitalizes all
nouns, regardless of whether they are names, and French and Spanish do not capitalize names that serve as
adjectives. Another name for this task is token classification.[30]
Sentiment analysis (see also Multimodal sentiment analysis)
Sentiment analysis is a computational method used to identify and classify the emotional intent behind text. This
technique involves analyzing text to determine whether the expressed sentiment is positive, negative, or neutral.
Models for sentiment classification typically utilize inputs such as word n-grams, Term Frequency-Inverse Document
Frequency (TF-IDF) features, hand-generated features, or employ deep learning models designed to recognize both
long-term and short-term dependencies in text sequences. The applications of sentiment analysis are diverse,
extending to tasks such as categorizing customer reviews on various online platforms.[26]
Terminology extraction
The goal of terminology extraction is to automatically extract relevant terms from a given corpus.
Word-sense disambiguation (WSD)
Many words have more than one meaning; we have to select the meaning which makes the most sense in context. For
this problem, we are typically given a list of words and associated word senses, e.g. from a dictionary or an
online resource such as WordNet.
Entity linking
Many words—typically proper names—refer to named entities; here we have to select the entity (a famous individual,
a location, a company, etc.) which is referred to in context.
Relational semantics (semantics of individual sentences)
Relationship extraction
Given a chunk of text, identify the relationships among named entities (e.g. who is married to whom).
Semantic parsing
Given a piece of text (typically a sentence), produce a formal representation of its semantics, either as a graph
(e.g., in AMR parsing) or in accordance with a logical formalism (e.g., in DRT parsing). This challenge typically
includes aspects of several more elementary NLP tasks from semantics (e.g., semantic role labelling, word-sense
disambiguation) and can be extended to include full-fledged discourse analysis (e.g., discourse analysis,
coreference; see Natural language understanding below).
Semantic role labelling (see also implicit semantic role labelling below)
Given a single sentence, identify and disambiguate semantic predicates (e.g., verbal frames), then identify and
classify the frame elements (semantic roles).
Discourse (semantics beyond individual sentences)
Coreference resolution
Given a sentence or larger chunk of text, determine which words ("mentions") refer to the same objects
("entities"). Anaphora resolution is a specific example of this task, and is specifically concerned with matching
up pronouns with the nouns or names to which they refer. The more general task of coreference resolution also
includes identifying so-called "bridging relationships" involving referring expressions. For example, in a sentence
such as "He entered John's house through the front door", "the front door" is a referring expression and the
bridging relationship to be identified is the fact that the door being referred to is the front door of John's
house (rather than of some other structure that might also be referred to).
Discourse analysis
This rubric includes several related tasks. One task is discourse parsing, i.e., identifying the discourse
structure of a connected text, i.e. the nature of the discourse relationships between sentences (e.g. elaboration,
explanation, contrast). Another possible task is recognizing and classifying the speech acts in a chunk of text
(e.g. yes–no question, content question, statement, assertion, etc.).
Implicit semantic role labelling
Given a single sentence, identify and disambiguate semantic predicates (e.g., verbal frames) and their explicit
semantic roles in the current sentence (see Semantic role labelling above). Then, identify semantic roles that are
not explicitly realized in the current sentence, classify them into arguments that are explicitly realized
elsewhere in the text and those that are not specified, and resolve the former against the local text. A closely
related task is zero anaphora resolution, i.e., the extension of coreference resolution to pro-drop languages.
Recognizing textual entailment
Given two text fragments, determine if one being true entails the other, entails the other's negation, or allows
the other to be either true or false.[31]
Topic segmentation and recognition
Given a chunk of text, separate it into segments each of which is devoted to a topic, and identify the topic of the
segment.
Argument mining
The goal of argument mining is the automatic extraction and identification of argumentative structures from natural
language text with the aid of computer programs.[32] Such argumentative structures include the premise,
conclusions, the argument scheme and the relationship between the main and subsidiary argument, or the main and
counter-argument within discourse.[33][34]
Higher-level NLP applications
Automatic summarization (text summarization)
Produce a readable summary of a chunk of text. Often used to provide summaries of the text of a known type, such
as research papers, articles in the financial section of a newspaper.
Grammatical error correction
Grammatical error detection and correction involves a great band-width of problems on all levels of linguistic
analysis (phonology/orthography, morphology, syntax, semantics, pragmatics). Grammatical error correction is
impactful since it affects hundreds of millions of people that use or acquire English as a second language. It has
thus been subject to a number of shared tasks since 2011.[35][36][37] As far as orthography, morphology, syntax and
certain aspects of semantics are concerned, and due to the development of powerful neural language models such as
GPT-2, this can now (2019) be considered a largely solved problem and is being marketed in various commercial
applications.
Logic translation
Translate a text from a natural language into formal logic.
Machine translation (MT)
Automatically translate text from one human language to another. This is one of the most difficult problems, and
is a member of a class of problems colloquially termed "AI-complete", i.e. requiring all of the different types of
knowledge that humans possess (grammar, semantics, facts about the real world, etc.) to solve properly.
Natural language understanding (NLU)
Convert chunks of text into more formal representations such as first-order logic structures that are easier for
computer programs to manipulate. Natural language understanding involves the identification of the intended
semantic from the multiple possible semantics which can be derived from a natural language expression which usually
takes the form of organized notations of natural language concepts. Introduction and creation of language metamodel
and ontology are efficient however empirical solutions. An explicit formalization of natural language semantics
without confusions with implicit assumptions such as closed-world assumption (CWA) vs. open-world assumption, or
subjective Yes/No vs. objective True/False is expected for the construction of a basis of semantics
formalization.[38]
Natural language generation (NLG):
Convert information from computer databases or semantic intents into readable human language.
Book generation
Not an NLP task proper but an extension of natural language generation and other NLP tasks is the creation of
full-fledged books. The first machine-generated book was created by a rule-based system in 1984 (Racter, The
policeman's beard is half-constructed).[39] The first published work by a neural network was published in 2018, 1
the Road, marketed as a novel, contains sixty million words. Both these systems are basically elaborate but
non-sensical (semantics-free) language models. The first machine-generated science book was published in 2019 (Beta
Writer, Lithium-Ion Batteries, Springer, Cham).[40] Unlike Racter and 1 the Road, this is grounded on factual
knowledge and based on text summarization.
Document AI
A Document AI platform sits on top of the NLP technology enabling users with no prior experience of artificial
intelligence, machine learning or NLP to quickly train a computer to extract the specific data they need from
different document types. NLP-powered Document AI enables non-technical teams to quickly access information hidden
in documents, for example, lawyers, business analysts and accountants.[41]
Dialogue management
Computer systems intended to converse with a human.
Question answering
Given a human-language question, determine its answer. Typical questions have a specific right answer (such as
"What is the capital of Canada?"), but sometimes open-ended questions are also considered (such as "What is the
meaning of life?").
Text-to-image generation
Given a description of an image, generate an image that matches the description.[42]
Text-to-scene generation
Given a description of a scene, generate a 3D model of the scene.[43][44]
Text-to-video
Given a description of a video, generate a video that matches the description.[45][46]
General tendencies and (possible) future directions
Based on long-standing trends in the field, it is possible to extrapolate future directions of NLP. As of 2020,
three trends among the topics of the long-standing series of CoNLL Shared Tasks can be observed:[47]
Interest on increasingly abstract, "cognitive" aspects of natural language (1999–2001: shallow parsing, 2002–03:
named entity recognition, 2006–09/2017–18: dependency syntax, 2004–05/2008–09 semantic role labelling, 2011–12
coreference, 2015–16: discourse parsing, 2019: semantic parsing).
Increasing interest in multilinguality, and, potentially, multimodality (English since 1999; Spanish, Dutch since
2002; German since 2003; Bulgarian, Danish, Japanese, Portuguese, Slovenian, Swedish, Turkish since 2006; Basque,
Catalan, Chinese, Greek, Hungarian, Italian, Turkish since 2007; Czech since 2009; Arabic since 2012; 2017: 40+
languages; 2018: 60+/100+ languages)
Elimination of symbolic representations (rule-based over supervised towards weakly supervised methods,
representation learning and end-to-end systems)
Cognition
Most higher-level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of
natural language. More broadly speaking, the technical operationalization of increasingly advanced aspects of
cognitive behaviour represents one of the developmental trajectories of NLP (see trends among CoNLL shared tasks
above).
Cognition refers to "the mental action or process of acquiring knowledge and understanding through thought,
experience, and the senses."[48] Cognitive science is the interdisciplinary, scientific study of the mind and its
processes.[49] Cognitive linguistics is an interdisciplinary branch of linguistics, combining knowledge and
research from both psychology and linguistics.[50] Especially during the age of symbolic NLP, the area of
computational linguistics maintained strong ties with cognitive studies.
As an example, George Lakoff offers a methodology to build natural language processing (NLP) algorithms through the
perspective of cognitive science, along with the findings of cognitive linguistics,[51] with two defining aspects:
Apply the theory of conceptual metaphor, explained by Lakoff as "the understanding of one idea, in terms of
another" which provides an idea of the intent of the author.[52] For example, consider the English word big. When
used in a comparison ("That is a big tree"), the author's intent is to imply that the tree is physically large
relative to other trees or the authors experience. When used metaphorically ("Tomorrow is a big day"), the
author's intent to imply importance. The intent behind other usages, like in "She is a big person", will remain
somewhat ambiguous to a person and a cognitive NLP algorithm alike without additional information.
Assign relative measures of meaning to a word, phrase, sentence or piece of text based on the information presented
before and after the piece of text being analyzed, e.g., by means of a probabilistic context-free grammar (PCFG).
The mathematical equation for such algorithms is presented in US Patent 9269353:[53]
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{\displaystyle {RMM(token_{N})}={PMM(token_{N})}\times {\frac {1}{2d}}\left(\sum
_{i=-d}^{d}{((PMM(token_{N})}\times {PF(token_{N-i},token_{N},token_{N+i}))_{i}}\right)}
Where
RMM is the relative measure of meaning
token is any block of text, sentence, phrase or word
N is the number of tokens being analyzed
PMM is the probable measure of meaning based on a corpora
d is the non zero location of the token along the sequence of N tokens
PF is the probability function specific to a language
Ties with cognitive linguistics are part of the historical heritage of NLP, but they have been less frequently
addressed since the statistical turn during the 1990s. Nevertheless, approaches to develop cognitive models towards
technically operationalizable frameworks have been pursued in the context of various frameworks, e.g., of cognitive
grammar,[54] functional grammar,[55] construction grammar,[56] computational psycholinguistics and cognitive
neuroscience (e.g., ACT-R), however, with limited uptake in mainstream NLP (as measured by presence on major
conferences[57] of the ACL). More recently, ideas of cognitive NLP have been revived as an approach to achieve
explainability, e.g., under the notion of "cognitive AI".[58] Likewise, ideas of cognitive NLP are inherent to
neural models multimodal NLP (although rarely made explicit)[59] and developments in artificial intelligence,
specifically tools and technologies using large language model approaches[60] and new directions in artificial
general intelligence based on the free energy principle[61] by British neuroscientist and theoretician at
University College London Karl J. Friston.
See also
1 the Road
Artificial intelligence detection software
Automated essay scoring
Biomedical text mining
Compound term processing
Computational linguistics
Computer-assisted reviewing
Controlled natural language
Deep learning
Deep linguistic processing
Distributional semantics
Foreign language reading aid
Foreign language writing aid
Information extraction
Information retrieval
Language and Communication Technologies
Language model
Language technology
Latent semantic indexing
Multi-agent system
Native-language identification
Natural-language programming
Natural-language understanding
Natural-language search
Outline of natural language processing
Query expansion
Query understanding
Reification (linguistics)
Speech processing
Spoken dialogue systems
Text-proofing
Text simplification
Transformer (machine learning model)
Truecasing
Question answering
Word2vec
References
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Eisenstein, Jacob (October 1, 2019). Introduction to Natural Language Processing. The MIT Press. p. 1. ISBN
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^ Joshi, A. K., & Weinstein, S. (1981, August). Control of Inference: Role of Some Aspects of Discourse
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as the so-called "poverty of the stimulus" argument entail that general learning algorithms, as are typically used
in machine learning, cannot be successful in language processing. As a result, the Chomskyan paradigm discouraged
the application of such models to language processing.
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^ "The Stanford Natural Language Processing Group". nlp.stanford.edu. Retrieved 2022-06-07.
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^ Friston, Karl J. (2022). Active Inference: The Free Energy Principle in Mind, Brain, and Behavior; Chapter 4 The
Generative Models of Active Inference. The MIT Press. ISBN 978-0-262-36997-8.
Further reading
Bates, M (1995). "Models of natural language understanding". Proceedings of the National Academy of Sciences of the
United States of America. 92 (22): 9977–9982. Bibcode:1995PNAS...92.9977B. doi:10.1073/pnas.92.22.9977. PMC 40721.
PMID 7479812.
Steven Bird, Ewan Klein, and Edward Loper (2009). Natural Language Processing with Python. O'Reilly Media. ISBN
978-0-596-51649-9.
Kenna Hughes-Castleberry, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has stumped humans
for decades, reveals the limitations of natural-language-processing algorithms", Scientific American, vol. 329, no.
4 (November 2023), pp. 81–82. "This murder mystery competition has revealed that although NLP (natural-language
processing) models are capable of incredible feats, their abilities are very much limited by the amount of context
they receive. This [...] could cause for researchers who hope to use them to do things such as analyze ancient
languages. In some cases, there are few historical records on long-gone civilizations to serve as training data for
such a purpose." (p. 82.)
Daniel Jurafsky and James H. Martin (2008). Speech and Language Processing, 2nd edition. Pearson Prentice Hall.
ISBN 978-0-13-187321-6.
Mohamed Zakaria Kurdi (2016). Natural Language Processing and Computational Linguistics: speech, morphology, and
syntax, Volume 1. ISTE-Wiley. ISBN 978-1848218482.
Mohamed Zakaria Kurdi (2017). Natural Language Processing and Computational Linguistics: semantics, discourse, and
applications, Volume 2. ISTE-Wiley. ISBN 978-1848219212.
Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze (2008). Introduction to Information Retrieval.
Cambridge University Press. ISBN 978-0-521-86571-5. Official html and pdf versions available without charge.
Christopher D. Manning and Hinrich Schütze (1999). Foundations of Statistical Natural Language Processing. The MIT
Press. ISBN 978-0-262-13360-9.
David M. W. Powers and Christopher C. R. Turk (1989). Machine Learning of Natural Language. Springer-Verlag. ISBN
978-0-387-19557-5.
External links
Media related to Natural language processing at Wikimedia Commons
vteNatural language processingGeneral terms
AI-complete
Bag-of-words
n-gram
Bigram
Trigram
Computational linguistics
Natural language understanding
Stop words
Text processing
Text analysis
Argument mining
Collocation extraction
Concept mining
Coreference resolution
Deep linguistic processing
Distant reading
Information extraction
Named-entity recognition
Ontology learning
Parsing
Semantic parsing
Syntactic parsing
Part-of-speech tagging
Semantic analysis
Semantic role labeling
Semantic decomposition
Semantic similarity
Sentiment analysis
Terminology extraction
Text mining
Textual entailment
Truecasing
Word-sense disambiguation
Word-sense induction
Text segmentation
Compound-term processing
Lemmatisation
Lexical analysis
Text chunking
Stemming
Sentence segmentation
Word segmentation
Automatic summarization
Multi-document summarization
Sentence extraction
Text simplification
Machine translation
Computer-assisted
Example-based
Rule-based
Statistical
Transfer-based
Neural
Distributional semantics models
BERT
Document-term matrix
Explicit semantic analysis
fastText
GloVe
Language model (large)
Latent semantic analysis
Seq2seq
Word embedding
Word2vec
Language resources,datasets and corporaTypes andstandards
Corpus linguistics
Lexical resource
Linguistic Linked Open Data
Machine-readable dictionary
Parallel text
PropBank
Semantic network
Simple Knowledge Organization System
Speech corpus
Text corpus
Thesaurus (information retrieval)
Treebank
Universal Dependencies
Data
BabelNet
Bank of English
DBpedia
FrameNet
Google Ngram Viewer
UBY
WordNet
Wikidata
Automatic identificationand data capture
Speech recognition
Speech segmentation
Speech synthesis
Natural language generation
Optical character recognition
Topic model
Document classification
Latent Dirichlet allocation
Pachinko allocation
Computer-assistedreviewing
Automated essay scoring
Concordancer
Grammar checker
Predictive text
Pronunciation assessment
Spell checker
Natural languageuser interface
Chatbot
Interactive fiction
Question answering
Virtual assistant
Voice user interface
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Natural Language Toolkit
spaCy
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Natural language processing
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text: str = sub(r"[^a-zA-Z0-9]", " ", paragraph)print(text)Natural language processing Wikipedia Jump to content Main menu Main menu move to sidebar hide Navigation Main pageContentsCurrent eventsRandom articleAbout WikipediaContact us Contribute HelpLearn to editCommunity portalRecent changesUpload fileSpecial pages Search Search Appearance Donate Create account Log in Personal tools Donate Create account Log in Pages for logged out editors learn more ContributionsTalk Contents move to sidebar hide Top 1 History Toggle History subsection 1 1 Symbolic NLP 1950s early 1990s 1 2 Statistical NLP 1990s present 2 Approaches Symbolic statistical neural networks Toggle Approaches Symbolic statistical neural networks subsection 2 1 Statistical approach 2 2 Neural networks 3 Common NLP tasks Toggle Common NLP tasks subsection 3 1 Text and speech processing 3 2 Morphological analysis 3 3 Syntactic analysis 3 4 Lexical semantics of individual words in context 3 5 Relational semantics semantics of individual sentences 3 6 Discourse semantics beyond individual sentences 3 7 Higher level NLP applications 4 General tendencies and possible future directions Toggle General tendencies and possible future directions subsection 4 1 Cognition 5 See also 6 References 7 Further reading 8 External links Toggle the table of contents Natural language processing 71 languages Afrikaans Az rbaycanca B n l m g BosanskiBrezhonegCatal e tinaCymraegDanskDeutschEesti Espa olEsperantoEuskara Fran aisGaeilgeGalego HrvatskiIdoBahasa IndonesiaIsiZulu slenskaItaliano Latvie uLietuvi Nederlands Norsk bokm l PicardPiemont isPolskiPortugu sQaraqalpaqshaRom n Runa Simi ShqipSimple English srpskiSrpskohrvatski Suomi T rk e Ti ng Vi t Edit links ArticleTalk English ReadEditView history Tools Tools move to sidebar hide Actions ReadEditView history General What links hereRelated changesUpload filePermanent linkPage informationCite this pageGet shortened URLDownload QR code Print export Download as PDFPrintable version In other projects Wikimedia CommonsWikiversityWikidata item Appearance move to sidebar hide From Wikipedia the free encyclopedia Processing of natural language by a computer This article has multiple issues Please help improve it or discuss these issues on the talk page Learn how and when to remove these messages This article needs additional citations for verification Please help improve this article by adding citations to reliable sources Unsourced material may be challenged and removed Find sources Natural language processing news newspapers books scholar JSTOR May 2024 Learn how and when to remove this message This article may need to be rewritten to comply with Wikipedia s quality standards You can help The talk page may contain suggestions July 2025 This article may be in need of reorganization to comply with Wikipedia s layout guidelines Please help by editing the article to make improvements to the overall structure July 2025 Learn how and when to remove this message Learn how and when to remove this message Natural language processing NLP is the processing of natural language information by a computer The study of NLP a subfield of computer science is generally associated with artificial intelligence NLP is related to information retrieval knowledge representation computational linguistics and more broadly with linguistics 1 Major processing tasks in an NLP system include speech recognition text classification natural language understanding and natural language generation History edit Further information History of natural language processing Natural language processing has its roots in the 1950s 2 Already in 1950 Alan Turing published an article titled Computing Machinery and Intelligence which proposed what is now called the Turing test as a criterion of intelligence though at the time that was not articulated as a problem separate from artificial intelligence The proposed test includes a task that involves the automated interpretation and generation of natural language Symbolic NLP 1950s early 1990s edit The premise of symbolic NLP is well summarized by John Searle s Chinese room experiment Given a collection of rules e g a Chinese phrasebook with questions and matching answers the computer emulates natural language understanding or other NLP tasks by applying those rules to the data it confronts 1950s The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English The authors claimed that within three or five years machine translation would be a solved problem 3 However real progress was much slower and after the ALPAC report in 1966 which found that ten years of research had failed to fulfill the expectations funding for machine translation was dramatically reduced Little further research in machine translation was conducted in America though some research continued elsewhere such as Japan and Europe 4 until the late 1980s when the first statistical machine translation systems were developed 1960s Some notably successful natural language processing systems developed in the 1960s were SHRDLU a natural language system working in restricted blocks worlds with restricted vocabularies and ELIZA a simulation of a Rogerian psychotherapist written by Joseph Weizenbaum between 1964 and 1966 Using almost no information about human thought or emotion ELIZA sometimes provided a startlingly human like interaction When the patient exceeded the very small knowledge base ELIZA might provide a generic response for example responding to My head hurts with Why do you say your head hurts Ross Quillian s successful work on natural language was demonstrated with a vocabulary of only twenty words because that was all that would fit in a computer memory at the time 5 1970s During the 1970s many programmers began to write conceptual ontologies which structured real world information into computer understandable data Examples are MARGIE Schank 1975 SAM Cullingford 1978 PAM Wilensky 1978 TaleSpin Meehan 1976 QUALM Lehnert 1977 Politics Carbonell 1979 and Plot Units Lehnert 1981 During this time the first chatterbots were written e g PARRY 1980s The 1980s and early 1990s mark the heyday of symbolic methods in NLP Focus areas of the time included research on rule based parsing e g the development of HPSG as a computational operationalization of generative grammar morphology e g two level morphology 6 semantics e g Lesk algorithm reference e g within Centering Theory 7 and other areas of natural language understanding e g in the Rhetorical Structure Theory Other lines of research were continued e g the development of chatterbots with Racter and Jabberwacky An important development that eventually led to the statistical turn in the 1990s was the rising importance of quantitative evaluation in this period 8 Statistical NLP 1990s present edit Up until the 1980s most natural language processing systems were based on complex sets of hand written rules Starting in the late 1980s however there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing This was due to both the steady increase in computational power see Moore s law and the gradual lessening of the dominance of Chomskyan theories of linguistics e g transformational grammar whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine learning approach to language processing 9 1990s Many of the notable early successes in statistical methods in NLP occurred in the field of machine translation due especially to work at IBM Research such as IBM alignment models These systems were able to take advantage of existing multilingual textual corpora that had been produced by the Parliament of Canada and the European Union as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government However most other systems depended on corpora specifically developed for the tasks implemented by these systems which was and often continues to be a major limitation in the success of these systems As a result a great deal of research has gone into methods of more effectively learning from limited amounts of data 2000s With the growth of the web increasing amounts of raw unannotated language data have become available since the mid 1990s Research has thus increasingly focused on unsupervised and semi supervised learning algorithms Such algorithms can learn from data that has not been hand annotated with the desired answers or using a combination of annotated and non annotated data Generally this task is much more difficult than supervised learning and typically produces less accurate results for a given amount of input data However there is an enormous amount of non annotated data available including among other things the entire content of the World Wide Web which can often make up for the worse efficiency if the algorithm used has a low enough time complexity to be practical 2003 word n gram model at the time the best statistical algorithm is outperformed by a multi layer perceptron with a single hidden layer and context length of several words trained on up to 14 million words by Bengio et al 10 2010 Tom Mikolov then a PhD student at Brno University of Technology with co authors applied a simple recurrent neural network with a single hidden layer to language modelling 11 and in the following years he went on to develop Word2vec In the 2010s representation learning and deep neural network style featuring many hidden layers machine learning methods became widespread in natural language processing That popularity was due partly to a flurry of results showing that such techniques 12 13 can achieve state of the art results in many natural language tasks e g in language modeling 14 and parsing 15 16 This is increasingly important in medicine and healthcare where NLP helps analyze notes and text in electronic health records that would otherwise be inaccessible for study when seeking to improve care 17 or protect patient privacy 18 Approaches Symbolic statistical neural networks edit Symbolic approach i e the hand coding of a set of rules for manipulating symbols coupled with a dictionary lookup was historically the first approach used both by AI in general and by NLP in particular 19 20 such as by writing grammars or devising heuristic rules for stemming Machine learning approaches which include both statistical and neural networks on the other hand have many advantages over the symbolic approach both statistical and neural networks methods can focus more on the most common cases extracted from a corpus of texts whereas the rule based approach needs to provide rules for both rare cases and common ones equally language models produced by either statistical or neural networks methods are more robust to both unfamiliar e g containing words or structures that have not been seen before and erroneous input e g with misspelled words or words accidentally omitted in comparison to the rule based systems which are also more costly to produce the larger such a probabilistic language model is the more accurate it becomes in contrast to rule based systems that can gain accuracy only by increasing the amount and complexity of the rules leading to intractability problems Rule based systems are commonly used when the amount of training data is insufficient to successfully apply machine learning methods e g for the machine translation of low resource languages such as provided by the Apertium system for preprocessing in NLP pipelines e g tokenization or for postprocessing and transforming the output of NLP pipelines e g for knowledge extraction from syntactic parses Statistical approach edit In the late 1980s and mid 1990s the statistical approach ended a period of AI winter which was caused by the inefficiencies of the rule based approaches 21 22 The earliest decision trees producing systems of hard if then rules were still very similar to the old rule based approaches Only the introduction of hidden Markov models applied to part of speech tagging announced the end of the old rule based approach Neural networks edit Further information Artificial neural network A major drawback of statistical methods is that they require elaborate feature engineering Since 2015 23 the statistical approach has been replaced by the neural networks approach using semantic networks 24 and word embeddings to capture semantic properties of words Intermediate tasks e g part of speech tagging and dependency parsing are not needed anymore Neural machine translation based on then newly invented sequence to sequence transformations made obsolete the intermediate steps such as word alignment previously necessary for statistical machine translation Common NLP tasks edit The following is a list of some of the most commonly researched tasks in natural language processing Some of these tasks have direct real world applications while others more commonly serve as subtasks that are used to aid in solving larger tasks Though natural language processing tasks are closely intertwined they can be subdivided into categories for convenience A coarse division is given below Text and speech processing edit Optical character recognition OCR Given an image representing printed text determine the corresponding text Speech recognition Given a sound clip of a person or people speaking determine the textual representation of the speech This is the opposite of text to speech and is one of the extremely difficult problems colloquially termed AI complete see above In natural speech there are hardly any pauses between successive words and thus speech segmentation is a necessary subtask of speech recognition see below In most spoken languages the sounds representing successive letters blend into each other in a process termed coarticulation so the conversion of the analog signal to discrete characters can be a very difficult process Also given that words in the same language are spoken by people with different accents the speech recognition software must be able to recognize the wide variety of input as being identical to each other in terms of its textual equivalent Speech segmentation Given a sound clip of a person or people speaking separate it into words A subtask of speech recognition and typically grouped with it Text to speech Given a text transform those units and produce a spoken representation Text to speech can be used to aid the visually impaired 25 Word segmentation Tokenization Tokenization is a process used in text analysis that divides text into individual words or word fragments This technique results in two key components a word index and tokenized text The word index is a list that maps unique words to specific numerical identifiers and the tokenized text replaces each word with its corresponding numerical token These numerical tokens are then used in various deep learning methods 26 For a language like English this is fairly trivial since words are usually separated by spaces However some written languages like Chinese Japanese and Thai do not mark word boundaries in such a fashion and in those languages text segmentation is a significant task requiring knowledge of the vocabulary and morphology of words in the language Sometimes this process is also used in cases like bag of words BOW creation in data mining citation needed Morphological analysis edit Lemmatization The task of removing inflectional endings only and to return the base dictionary form of a word which is also known as a lemma Lemmatization is another technique for reducing words to their normalized form But in this case the transformation actually uses a dictionary to map words to their actual form 27 Morphological segmentation Separate words into individual morphemes and identify the class of the morphemes The difficulty of this task depends greatly on the complexity of the morphology i e the structure of words of the language being considered English has fairly simple morphology especially inflectional morphology and thus it is often possible to ignore this task entirely and simply model all possible forms of a word e g open opens opened opening as separate words In languages such as Turkish or Meitei a highly agglutinated Indian language however such an approach is not possible as each dictionary entry has thousands of possible word forms 28 Part of speech tagging Given a sentence determine the part of speech POS for each word Many words especially common ones can serve as multiple parts of speech For example book can be a noun the book on the table or verb to book a flight set can be a noun verb or adjective and out can be any of at least five different parts of speech Stemming The process of reducing inflected or sometimes derived words to a base form e g close will be the root for closed closing close closer etc Stemming yields similar results as lemmatization but does so on grounds of rules not a dictionary Syntactic analysis edit Part of a series onFormal languages Key concepts Formal system Alphabet Syntax Formal semantics Semantics programming languages Formal grammar Formation rule Well formed formula Automata theory Regular expression Production Ground expression Atomic formula Applications Formal methods Propositional calculus Predicate logic Mathematical notation Natural language processing Programming language theory Mathematical linguistics Computational linguistics Syntax analysis Formal verification Automated theorem proving vte Grammar induction 29 Generate a formal grammar that describes a language s syntax Sentence breaking also known as sentence boundary disambiguation Given a chunk of text find the sentence boundaries Sentence boundaries are often marked by periods or other punctuation marks but these same characters can serve other purposes e g marking abbreviations Parsing Determine the parse tree grammatical analysis of a given sentence The grammar for natural languages is ambiguous and typical sentences have multiple possible analyses perhaps surprisingly for a typical sentence there may be thousands of potential parses most of which will seem completely nonsensical to a human There are two primary types of parsing dependency parsing and constituency parsing Dependency parsing focuses on the relationships between words in a sentence marking things like primary objects and predicates whereas constituency parsing focuses on building out the parse tree using a probabilistic context free grammar PCFG see also stochastic grammar Lexical semantics of individual words in context edit Lexical semantics What is the computational meaning of individual words in context Distributional semantics How can we learn semantic representations from data Named entity recognition NER Given a stream of text determine which items in the text map to proper names such as people or places and what the type of each such name is e g person location organization Although capitalization can aid in recognizing named entities in languages such as English this information cannot aid in determining the type of named entity and in any case is often inaccurate or insufficient For example the first letter of a sentence is also capitalized and named entities often span several words only some of which are capitalized Furthermore many other languages in non Western scripts e g Chinese or Arabic do not have any capitalization at all and even languages with capitalization may not consistently use it to distinguish names For example German capitalizes all nouns regardless of whether they are names and French and Spanish do not capitalize names that serve as adjectives Another name for this task is token classification 30 Sentiment analysis see also Multimodal sentiment analysis Sentiment analysis is a computational method used to identify and classify the emotional intent behind text This technique involves analyzing text to determine whether the expressed sentiment is positive negative or neutral Models for sentiment classification typically utilize inputs such as word n grams Term Frequency Inverse Document Frequency TF IDF features hand generated features or employ deep learning models designed to recognize both long term and short term dependencies in text sequences The applications of sentiment analysis are diverse extending to tasks such as categorizing customer reviews on various online platforms 26 Terminology extraction The goal of terminology extraction is to automatically extract relevant terms from a given corpus Word sense disambiguation WSD Many words have more than one meaning we have to select the meaning which makes the most sense in context For this problem we are typically given a list of words and associated word senses e g from a dictionary or an online resource such as WordNet Entity linking Many words typically proper names refer to named entities here we have to select the entity a famous individual a location a company etc which is referred to in context Relational semantics semantics of individual sentences edit Relationship extraction Given a chunk of text identify the relationships among named entities e g who is married to whom Semantic parsing Given a piece of text typically a sentence produce a formal representation of its semantics either as a graph e g in AMR parsing or in accordance with a logical formalism e g in DRT parsing This challenge typically includes aspects of several more elementary NLP tasks from semantics e g semantic role labelling word sense disambiguation and can be extended to include full fledged discourse analysis e g discourse analysis coreference see Natural language understanding below Semantic role labelling see also implicit semantic role labelling below Given a single sentence identify and disambiguate semantic predicates e g verbal frames then identify and classify the frame elements semantic roles Discourse semantics beyond individual sentences edit Coreference resolution Given a sentence or larger chunk of text determine which words mentions refer to the same objects entities Anaphora resolution is a specific example of this task and is specifically concerned with matching up pronouns with the nouns or names to which they refer The more general task of coreference resolution also includes identifying so called bridging relationships involving referring expressions For example in a sentence such as He entered John s house through the front door the front door is a referring expression and the bridging relationship to be identified is the fact that the door being referred to is the front door of John s house rather than of some other structure that might also be referred to Discourse analysis This rubric includes several related tasks One task is discourse parsing i e identifying the discourse structure of a connected text i e the nature of the discourse relationships between sentences e g elaboration explanation contrast Another possible task is recognizing and classifying the speech acts in a chunk of text e g yes no question content question statement assertion etc Implicit semantic role labelling Given a single sentence identify and disambiguate semantic predicates e g verbal frames and their explicit semantic roles in the current sentence see Semantic role labelling above Then identify semantic roles that are not explicitly realized in the current sentence classify them into arguments that are explicitly realized elsewhere in the text and those that are not specified and resolve the former against the local text A closely related task is zero anaphora resolution i e the extension of coreference resolution to pro drop languages Recognizing textual entailment Given two text fragments determine if one being true entails the other entails the other s negation or allows the other to be either true or false 31 Topic segmentation and recognition Given a chunk of text separate it into segments each of which is devoted to a topic and identify the topic of the segment Argument mining The goal of argument mining is the automatic extraction and identification of argumentative structures from natural language text with the aid of computer programs 32 Such argumentative structures include the premise conclusions the argument scheme and the relationship between the main and subsidiary argument or the main and counter argument within discourse 33 34 Higher level NLP applications edit Automatic summarization text summarization Produce a readable summary of a chunk of text Often used to provide summaries of the text of a known type such as research papers articles in the financial section of a newspaper Grammatical error correction Grammatical error detection and correction involves a great band width of problems on all levels of linguistic analysis phonology orthography morphology syntax semantics pragmatics Grammatical error correction is impactful since it affects hundreds of millions of people that use or acquire English as a second language It has thus been subject to a number of shared tasks since 2011 35 36 37 As far as orthography morphology syntax and certain aspects of semantics are concerned and due to the development of powerful neural language models such as GPT 2 this can now 2019 be considered a largely solved problem and is being marketed in various commercial applications Logic translation Translate a text from a natural language into formal logic Machine translation MT Automatically translate text from one human language to another This is one of the most difficult problems and is a member of a class of problems colloquially termed AI complete i e requiring all of the different types of knowledge that humans possess grammar semantics facts about the real world etc to solve properly Natural language understanding NLU Convert chunks of text into more formal representations such as first order logic structures that are easier for computer programs to manipulate Natural language understanding involves the identification of the intended semantic from the multiple possible semantics which can be derived from a natural language expression which usually takes the form of organized notations of natural language concepts Introduction and creation of language metamodel and ontology are efficient however empirical solutions An explicit formalization of natural language semantics without confusions with implicit assumptions such as closed world assumption CWA vs open world assumption or subjective Yes No vs objective True False is expected for the construction of a basis of semantics formalization 38 Natural language generation NLG Convert information from computer databases or semantic intents into readable human language Book generation Not an NLP task proper but an extension of natural language generation and other NLP tasks is the creation of full fledged books The first machine generated book was created by a rule based system in 1984 Racter The policeman s beard is half constructed 39 The first published work by a neural network was published in 2018 1 the Road marketed as a novel contains sixty million words Both these systems are basically elaborate but non sensical semantics free language models The first machine generated science book was published in 2019 Beta Writer Lithium Ion Batteries Springer Cham 40 Unlike Racter and 1 the Road this is grounded on factual knowledge and based on text summarization Document AI A Document AI platform sits on top of the NLP technology enabling users with no prior experience of artificial intelligence machine learning or NLP to quickly train a computer to extract the specific data they need from different document types NLP powered Document AI enables non technical teams to quickly access information hidden in documents for example lawyers business analysts and accountants 41 Dialogue management Computer systems intended to converse with a human Question answering Given a human language question determine its answer Typical questions have a specific right answer such as What is the capital of Canada but sometimes open ended questions are also considered such as What is the meaning of life Text to image generation Given a description of an image generate an image that matches the description 42 Text to scene generation Given a description of a scene generate a 3D model of the scene 43 44 Text to video Given a description of a video generate a video that matches the description 45 46 General tendencies and possible future directions edit Based on long standing trends in the field it is possible to extrapolate future directions of NLP As of 2020 three trends among the topics of the long standing series of CoNLL Shared Tasks can be observed 47 Interest on increasingly abstract cognitive aspects of natural language 1999 2001 shallow parsing 2002 03 named entity recognition 2006 09 2017 18 dependency syntax 2004 05 2008 09 semantic role labelling 2011 12 coreference 2015 16 discourse parsing 2019 semantic parsing Increasing interest in multilinguality and potentially multimodality English since 1999 Spanish Dutch since 2002 German since 2003 Bulgarian Danish Japanese Portuguese Slovenian Swedish Turkish since 2006 Basque Catalan Chinese Greek Hungarian Italian Turkish since 2007 Czech since 2009 Arabic since 2012 2017 40 languages 2018 60 100 languages Elimination of symbolic representations rule based over supervised towards weakly supervised methods representation learning and end to end systems Cognition edit Most higher level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language More broadly speaking the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of NLP see trends among CoNLL shared tasks above Cognition refers to the mental action or process of acquiring knowledge and understanding through thought experience and the senses 48 Cognitive science is the interdisciplinary scientific study of the mind and its processes 49 Cognitive linguistics is an interdisciplinary branch of linguistics combining knowledge and research from both psychology and linguistics 50 Especially during the age of symbolic NLP the area of computational linguistics maintained strong ties with cognitive studies As an example George Lakoff offers a methodology to build natural language processing NLP algorithms through the perspective of cognitive science along with the findings of cognitive linguistics 51 with two defining aspects Apply the theory of conceptual metaphor explained by Lakoff as the understanding of one idea in terms of another which provides an idea of the intent of the author 52 For example consider the English word big When used in a comparison That is a big tree the author s intent is to imply that the tree is physically large relative to other trees or the authors experience When used metaphorically Tomorrow is a big day the author s intent to imply importance The intent behind other usages like in She is a big person will remain somewhat ambiguous to a person and a cognitive NLP algorithm alike without additional information Assign relative measures of meaning to a word phrase sentence or piece of text based on the information presented before and after the piece of text being analyzed e g by means of a probabilistic context free grammar PCFG The mathematical equation for such algorithms is presented in US Patent 9269353 53 R M M t o k e n N P M M t o k e n N 1 2 d i d d P M M t o k e n N P F t o k e n N i t o k e n N t o k e n N i i displaystyle RMM token N PMM token N times frac 1 2d left sum i d d PMM token N times PF token N i token N token N i i right Where RMM is the relative measure of meaning token is any block of text sentence phrase or word N is the number of tokens being analyzed PMM is the probable measure of meaning based on a corpora d is the non zero location of the token along the sequence of N tokens PF is the probability function specific to a language Ties with cognitive linguistics are part of the historical heritage of NLP but they have been less frequently addressed since the statistical turn during the 1990s Nevertheless approaches to develop cognitive models towards technically operationalizable frameworks have been pursued in the context of various frameworks e g of cognitive grammar 54 functional grammar 55 construction grammar 56 computational psycholinguistics and cognitive neuroscience e g ACT R however with limited uptake in mainstream NLP as measured by presence on major conferences 57 of the ACL More recently ideas of cognitive NLP have been revived as an approach to achieve explainability e g under the notion of cognitive AI 58 Likewise ideas of cognitive NLP are inherent to neural models multimodal NLP although rarely made explicit 59 and developments in artificial intelligence specifically tools and technologies using large language model approaches 60 and new directions in artificial general intelligence based on the free energy principle 61 by British neuroscientist and theoretician at University College London Karl J Friston See also edit 1 the Road Artificial intelligence detection software Automated essay scoring Biomedical text mining Compound term processing Computational linguistics Computer assisted reviewing Controlled natural language Deep learning Deep linguistic processing Distributional semantics Foreign language reading aid Foreign language writing aid Information extraction Information retrieval Language and Communication Technologies Language model Language technology Latent semantic indexing Multi agent system Native language identification Natural language programming Natural language understanding Natural language search Outline of natural language processing Query expansion Query understanding Reification linguistics Speech processing Spoken dialogue systems Text proofing Text simplification Transformer machine learning model Truecasing Question answering Word2vec References edit Eisenstein Jacob October 1 2019 Introduction to Natural Language Processing The MIT Press p 1 ISBN 9780262042840 NLP Hutchins J 2005 The history of machine translation in a nutshell PDF self published source ALPAC the in famous report John Hutchins MT News International no 14 June 1996 pp 9 12 Crevier 1993 pp 146 148 harvnb error no target CITEREFCrevier1993 help see also Buchanan 2005 p 56 harvnb error no target CITEREFBuchanan2005 help Early programs were necessarily limited in scope by the size and speed of memory Koskenniemi Kimmo 1983 Two level morphology A general computational model of word form recognition and production PDF Department of General Linguistics University of Helsinki Joshi A K Weinstein S 1981 August Control of Inference Role of Some Aspects of Discourse Structure Centering In IJCAI pp 385 387 Guida G Mauri G July 1986 Evaluation of natural language processing systems Issues and approaches Proceedings of the IEEE 74 7 1026 1035 doi 10 1109 PROC 1986 13580 ISSN 1558 2256 S2CID 30688575 Chomskyan linguistics encourages the investigation of corner cases that stress the limits of its theoretical models comparable to pathological phenomena in mathematics typically created using thought experiments rather than the systematic investigation of typical phenomena that occur in real world data as is the case in corpus linguistics The creation and use of such corpora of real world data is a fundamental part of machine learning algorithms for natural language processing In addition theoretical underpinnings of Chomskyan linguistics such as the so called poverty of the stimulus argument entail that general learning algorithms as are typically used in machine learning cannot be successful in language processing As a result the Chomskyan paradigm discouraged the application of such models to language processing Bengio Yoshua Ducharme R jean Vincent Pascal Janvin Christian March 1 2003 A neural probabilistic language model The Journal of Machine Learning Research 3 1137 1155 via ACM Digital Library Mikolov Tom Karafi t Martin Burget Luk ernock Jan Khudanpur Sanjeev 26 September 2010 Recurrent neural network based language model PDF Interspeech 2010 pp 1045 1048 doi 10 21437 Interspeech 2010 343 S2CID 17048224 cite book journal ignored help Goldberg Yoav 2016 A Primer on Neural Network Models for Natural Language Processing Journal of Artificial Intelligence Research 57 345 420 arXiv 1807 10854 doi 10 1613 jair 4992 S2CID 8273530 Goodfellow Ian Bengio Yoshua Courville Aaron 2016 Deep Learning MIT Press Jozefowicz Rafal Vinyals Oriol Schuster Mike Shazeer Noam Wu Yonghui 2016 Exploring the Limits of Language Modeling arXiv 1602 02410 Bibcode 2016arXiv160202410J Choe Do Kook Charniak Eugene Parsing as Language Modeling Emnlp 2016 Archived from the original on 2018 10 23 Retrieved 2018 10 22 Vinyals Oriol et al 2014 Grammar as a Foreign Language PDF Nips2015 arXiv 1412 7449 Bibcode 2014arXiv1412 7449V Turchin Alexander Florez Builes Luisa F 2021 03 19 Using Natural Language Processing to Measure and Improve Quality of Diabetes Care A Systematic Review Journal of Diabetes Science and Technology 15 3 553 560 doi 10 1177 19322968211000831 ISSN 1932 2968 PMC 8120048 PMID 33736486 Lee Jennifer Yang Samuel Holland Hall Cynthia Sezgin Emre Gill Manjot Linwood Simon Huang Yungui Hoffman Jeffrey 2022 06 10 Prevalence of Sensitive Terms in Clinical Notes Using Natural Language Processing Techniques Observational Study JMIR Medical Informatics 10 6 e38482 doi 10 2196 38482 ISSN 2291 9694 PMC 9233261 PMID 35687381 Winograd Terry 1971 Procedures as a Representation for Data in a Computer Program for Understanding Natural Language Thesis Schank Roger C Abelson Robert P 1977 Scripts Plans Goals and Understanding An Inquiry Into Human Knowledge Structures Hillsdale Erlbaum ISBN 0 470 99033 3 Mark Johnson How the statistical revolution changes computational linguistics Proceedings of the EACL 2009 Workshop on the Interaction between Linguistics and Computational Linguistics Philip Resnik Four revolutions Language Log February 5 2011 Socher Richard Deep Learning For NLP ACL 2012 Tutorial www socher org Retrieved 2020 08 17 This was an early Deep Learning tutorial at the ACL 2012 and met with both interest and at the time skepticism by most participants Until then neural learning was basically rejected because of its lack of statistical interpretability Until 2015 deep learning had evolved into the major framework of NLP Link is broken try http web stanford edu class cs224n Segev Elad 2022 Semantic Network Analysis in Social Sciences London Routledge ISBN 9780367636524 Archived from the original on 5 December 2021 Retrieved 5 December 2021 Yi Chucai Tian Yingli 2012 Assistive Text Reading from Complex Background for Blind Persons Camera Based Document Analysis and Recognition Lecture Notes in Computer Science vol 7139 Springer Berlin Heidelberg pp 15 28 CiteSeerX 10 1 1 668 869 doi 10 1007 978 3 642 29364 1 2 ISBN 9783642293634 a b Natural Language Processing NLP A Complete Guide www deeplearning ai 2023 01 11 Retrieved 2024 05 05 What is Natural Language Processing Intro to NLP in Machine Learning GyanSetu 2020 12 06 Retrieved 2021 01 09 Kishorjit N Vidya Raj RK Nirmal Y Sivaji B 2012 Manipuri Morpheme Identification PDF Proceedings of the 3rd Workshop on South and Southeast Asian Natural Language Processing SANLP COLING 2012 Mumbai December 2012 95 108 cite journal CS1 maint location link Klein Dan Manning Christopher D 2002 Natural language grammar induction using a constituent context model PDF Advances in Neural Information Processing Systems Kariampuzha William Alyea Gioconda Qu Sue Sanjak Jaleal Math Ewy Sid Eric Chatelaine Haley Yadaw Arjun Xu Yanji Zhu Qian 2023 Precision information extraction for rare disease epidemiology at scale Journal of Translational Medicine 21 1 157 doi 10 1186 s12967 023 04011 y PMC 9972634 PMID 36855134 PASCAL Recognizing Textual Entailment Challenge RTE 7 https tac nist gov 2011 RTE Lippi Marco Torroni Paolo 2016 04 20 Argumentation Mining State of the Art and Emerging Trends ACM Transactions on Internet Technology 16 2 1 25 doi 10 1145 2850417 hdl 11585 523460 ISSN 1533 5399 S2CID 9561587 Argument Mining IJCAI2016 Tutorial www i3s unice fr Retrieved 2021 03 09 NLP Approaches to Computational Argumentation ACL 2016 Berlin Retrieved 2021 03 09 Administration Centre for Language Technology CLT Macquarie University Retrieved 2021 01 11 Shared Task Grammatical Error Correction www comp nus edu sg Retrieved 2021 01 11 Shared Task Grammatical Error Correction www comp nus edu sg Retrieved 2021 01 11 Duan Yucong Cruz Christophe 2011 Formalizing Semantic of Natural Language through Conceptualization from Existence International Journal of Innovation Management and Technology 2 1 37 42 Archived from the original on 2011 10 09 U B U W E B Racter www ubu com Retrieved 2020 08 17 Writer Beta 2019 Lithium Ion Batteries doi 10 1007 978 3 030 16800 1 ISBN 978 3 030 16799 8 S2CID 155818532 Document Understanding AI on Google Cloud Cloud Next 19 YouTube www youtube com 11 April 2019 Archived from the original on 2021 10 30 Retrieved 2021 01 11 Robertson Adi 2022 04 06 OpenAI s DALL E AI image generator can now edit pictures too The Verge Retrieved 2022 06 07 The Stanford Natural Language Processing Group nlp stanford edu Retrieved 2022 06 07 Coyne Bob Sproat Richard 2001 08 01 WordsEye Proceedings of the 28th annual conference on Computer graphics and interactive techniques SIGGRAPH 01 New York NY USA Association for Computing Machinery pp 487 496 doi 10 1145 383259 383316 ISBN 978 1 58113 374 5 S2CID 3842372 Google announces AI advances in text to video language translation more VentureBeat 2022 11 02 Retrieved 2022 11 09 Vincent James 2022 09 29 Meta s new text to video AI generator is like DALL E for video The Verge Retrieved 2022 11 09 Previous shared tasks CoNLL www conll org Retrieved 2021 01 11 Cognition Lexico Oxford University Press and Dictionary com Archived from the original on July 15 2020 Retrieved 6 May 2020 Ask the Cognitive Scientist American Federation of Teachers 8 August 2014 Cognitive science is an interdisciplinary field of researchers from Linguistics psychology neuroscience philosophy computer science and anthropology that seek to understand the mind Robinson Peter 2008 Handbook of Cognitive Linguistics and Second Language Acquisition Routledge pp 3 8 ISBN 978 0 805 85352 0 Lakoff George 1999 Philosophy in the Flesh The Embodied Mind and Its Challenge to Western Philosophy Appendix The Neural Theory of Language Paradigm New York Basic Books pp 569 583 ISBN 978 0 465 05674 3 Strauss Claudia 1999 A Cognitive Theory of Cultural Meaning Cambridge University Press pp 156 164 ISBN 978 0 521 59541 4 US patent 9269353 Universal Conceptual Cognitive Annotation UCCA Universal Conceptual Cognitive Annotation UCCA Retrieved 2021 01 11 Rodr guez F C Mairal Us n R 2016 Building an RRG computational grammar Onomazein 34 86 117 Fluid Construction Grammar A fully operational processing system for construction grammars Retrieved 2021 01 11 ACL Member Portal The Association for Computational Linguistics Member Portal www aclweb org Retrieved 2021 01 11 Chunks and Rules W3C Retrieved 2021 01 11 Socher Richard Karpathy Andrej Le Quoc V Manning Christopher D Ng Andrew Y 2014 Grounded Compositional Semantics for Finding and Describing Images with Sentences Transactions of the Association for Computational Linguistics 2 207 218 doi 10 1162 tacl a 00177 S2CID 2317858 Dasgupta Ishita Lampinen Andrew K Chan Stephanie C Y Creswell Antonia Kumaran Dharshan McClelland James L Hill Felix 2022 Language models show human like content effects on reasoning Dasgupta Lampinen et al arXiv 2207 07051 cs CL Friston Karl J 2022 Active Inference The Free Energy Principle in Mind Brain and Behavior Chapter 4 The Generative Models of Active Inference The MIT Press ISBN 978 0 262 36997 8 Further reading edit Bates M 1995 Models of natural language understanding Proceedings of the National Academy of Sciences of the United States of America 92 22 9977 9982 Bibcode 1995PNAS 92 9977B doi 10 1073 pnas 92 22 9977 PMC 40721 PMID 7479812 Steven Bird Ewan Klein and Edward Loper 2009 Natural Language Processing with Python O Reilly Media ISBN 978 0 596 51649 9 Kenna Hughes Castleberry A Murder Mystery Puzzle The literary puzzle Cain s Jawbone which has stumped humans for decades reveals the limitations of natural language processing algorithms Scientific American vol 329 no 4 November 2023 pp 81 82 This murder mystery competition has revealed that although NLP natural language processing models are capable of incredible feats their abilities are very much limited by the amount of context they receive This could cause difficulties for researchers who hope to use them to do things such as analyze ancient languages In some cases there are few historical records on long gone civilizations to serve as training data for such a purpose p 82 Daniel Jurafsky and James H Martin 2008 Speech and Language Processing 2nd edition Pearson Prentice Hall ISBN 978 0 13 187321 6 Mohamed Zakaria Kurdi 2016 Natural Language Processing and Computational Linguistics speech morphology and syntax Volume 1 ISTE Wiley ISBN 978 1848218482 Mohamed Zakaria Kurdi 2017 Natural Language Processing and Computational Linguistics semantics discourse and applications Volume 2 ISTE Wiley ISBN 978 1848219212 Christopher D Manning Prabhakar Raghavan and Hinrich Sch tze 2008 Introduction to Information Retrieval Cambridge University Press ISBN 978 0 521 86571 5 Official html and pdf versions available without charge Christopher D Manning and Hinrich Sch tze 1999 Foundations of Statistical Natural Language Processing The MIT Press ISBN 978 0 262 13360 9 David M W Powers and Christopher C R Turk 1989 Machine Learning of Natural Language Springer Verlag ISBN 978 0 387 19557 5 External links edit Media related to Natural language processing at Wikimedia Commons vteNatural language processingGeneral terms AI complete Bag of words n gram Bigram Trigram Computational linguistics Natural language understanding Stop words Text processing Text analysis Argument mining Collocation extraction Concept mining Coreference resolution Deep linguistic processing Distant reading Information extraction Named entity recognition Ontology learning Parsing Semantic parsing Syntactic parsing Part of speech tagging Semantic analysis Semantic role labeling Semantic decomposition Semantic similarity Sentiment analysis Terminology extraction Text mining Textual entailment Truecasing Word sense disambiguation Word sense induction Text segmentation Compound term processing Lemmatisation Lexical analysis Text chunking Stemming Sentence segmentation Word segmentation Automatic summarization Multi document summarization Sentence extraction Text simplification Machine translation Computer assisted Example based Rule based Statistical Transfer based Neural Distributional semantics models BERT Document term matrix Explicit semantic analysis fastText GloVe Language model large Latent semantic analysis Seq2seq Word embedding Word2vec Language resources datasets and corporaTypes andstandards Corpus linguistics Lexical resource Linguistic Linked Open Data Machine readable dictionary Parallel text PropBank Semantic network Simple Knowledge Organization System Speech corpus Text corpus Thesaurus information retrieval Treebank Universal Dependencies Data BabelNet Bank of English DBpedia FrameNet Google Ngram Viewer UBY WordNet Wikidata Automatic identificationand data capture Speech recognition Speech segmentation Speech synthesis Natural language generation Optical character recognition Topic model Document classification Latent Dirichlet allocation Pachinko allocation Computer assistedreviewing Automated essay scoring Concordancer Grammar checker Predictive text Pronunciation assessment Spell checker Natural languageuser interface Chatbot Interactive fiction Question answering Virtual assistant Voice user interface Related Formal semantics Hallucination Natural Language Toolkit spaCy Portal Language Authority control databases NationalUnited StatesJapanCzech RepublicIsraelOtherYale LUX Retrieved from https en wikipedia org w index php title Natural language processing oldid 1301380737 Categories Natural language processingComputational fields of studyComputational linguisticsSpeech recognitionHidden categories All accuracy disputesAccuracy disputes from December 2013Harv and Sfn no target errorsCS1 errors periodical ignoredCS1 maint locationArticles with short descriptionShort description is different from WikidataArticles needing additional references from May 2024All articles needing additional referencesWikipedia articles needing rewrite from July 2025All articles needing rewriteWikipedia articles needing reorganization from July 2025Articles with multiple maintenance issuesAll articles with unsourced statementsArticles with unsourced statements from May 2024Commons category link from Wikidata This page was last edited on 19 July 2025 at 13 48 UTC Text is available under the Creative Commons Attribution ShareAlike 4 0 License additional terms may apply By using this site you agree to the Terms of Use and Privacy Policy Wikipedia is a registered trademark of the Wikimedia Foundation Inc a non profit organization Privacy policy About Wikipedia Disclaimers Contact Wikipedia Code of Conduct Developers Statistics Cookie statement Mobile view Search Search Toggle the table of contents Natural language processing 71 languages Add topic
text_without_whitespace: str = sub(r"\s+", " ", text)print(text_without_whitespace)Natural language processing Wikipedia Jump to content Main menu Main menu move to sidebar hide Navigation Main pageContentsCurrent eventsRandom articleAbout WikipediaContact us Contribute HelpLearn to editCommunity portalRecent changesUpload fileSpecial pages Search Search Appearance Donate Create account Log in Personal tools Donate Create account Log in Pages for logged out editors learn more ContributionsTalk Contents move to sidebar hide Top 1 History Toggle History subsection 1 1 Symbolic NLP 1950s early 1990s 1 2 Statistical NLP 1990s present 2 Approaches Symbolic statistical neural networks Toggle Approaches Symbolic statistical neural networks subsection 2 1 Statistical approach 2 2 Neural networks 3 Common NLP tasks Toggle Common NLP tasks subsection 3 1 Text and speech processing 3 2 Morphological analysis 3 3 Syntactic analysis 3 4 Lexical semantics of individual words in context 3 5 Relational semantics semantics of individual sentences 3 6 Discourse semantics beyond individual sentences 3 7 Higher level NLP applications 4 General tendencies and possible future directions Toggle General tendencies and possible future directions subsection 4 1 Cognition 5 See also 6 References 7 Further reading 8 External links Toggle the table of contents Natural language processing 71 languages Afrikaans Az rbaycanca B n l m g BosanskiBrezhonegCatal e tinaCymraegDanskDeutschEesti Espa olEsperantoEuskara Fran aisGaeilgeGalego HrvatskiIdoBahasa IndonesiaIsiZulu slenskaItaliano Latvie uLietuvi Nederlands Norsk bokm l PicardPiemont isPolskiPortugu sQaraqalpaqshaRom n Runa Simi ShqipSimple English srpskiSrpskohrvatski Suomi T rk e Ti ng Vi t Edit links ArticleTalk English ReadEditView history Tools Tools move to sidebar hide Actions ReadEditView history General What links hereRelated changesUpload filePermanent linkPage informationCite this pageGet shortened URLDownload QR code Print 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and when to remove this message Learn how and when to remove this message Natural language processing NLP is the processing of natural language information by a computer The study of NLP a subfield of computer science is generally associated with artificial intelligence NLP is related to information retrieval knowledge representation computational linguistics and more broadly with linguistics 1 Major processing tasks in an NLP system include speech recognition text classification natural language understanding and natural language generation History edit Further information History of natural language processing Natural language processing has its roots in the 1950s 2 Already in 1950 Alan Turing published an article titled Computing Machinery and Intelligence which proposed what is now called the Turing test as a criterion of intelligence though at the time that was not articulated as a problem separate from artificial intelligence The proposed test includes a task that involves the automated interpretation and generation of natural language Symbolic NLP 1950s early 1990s edit The premise of symbolic NLP is well summarized by John Searle s Chinese room experiment Given a collection of rules e g a Chinese phrasebook with questions and matching answers the computer emulates natural language understanding or other NLP tasks by applying those rules to the data it confronts 1950s The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English The authors claimed that within three or five years machine translation would be a solved problem 3 However real progress was much slower and after the ALPAC report in 1966 which found that ten years of research had failed to fulfill the expectations funding for machine translation was dramatically reduced Little further research in machine translation was conducted in America though some research continued elsewhere such as Japan and Europe 4 until the late 1980s when the first statistical machine translation systems were developed 1960s Some notably successful natural language processing systems developed in the 1960s were SHRDLU a natural language system working in restricted blocks worlds with restricted vocabularies and ELIZA a simulation of a Rogerian psychotherapist written by Joseph Weizenbaum between 1964 and 1966 Using almost no information about human thought or emotion ELIZA sometimes provided a startlingly human like interaction When the patient exceeded the very small knowledge base ELIZA might provide a generic response for example responding to My head hurts with Why do you say your head hurts Ross Quillian s successful work on natural language was demonstrated with a vocabulary of only twenty words because that was all that would fit in a computer memory at the time 5 1970s During the 1970s many programmers began to write conceptual ontologies which structured real world information into computer understandable data Examples are MARGIE Schank 1975 SAM Cullingford 1978 PAM Wilensky 1978 TaleSpin Meehan 1976 QUALM Lehnert 1977 Politics Carbonell 1979 and Plot Units Lehnert 1981 During this time the first chatterbots were written e g PARRY 1980s The 1980s and early 1990s mark the heyday of symbolic methods in NLP Focus areas of the time included research on rule based parsing e g the development of HPSG as a computational operationalization of generative grammar morphology e g two level morphology 6 semantics e g Lesk algorithm reference e g within Centering Theory 7 and other areas of natural language understanding e g in the Rhetorical Structure Theory Other lines of research were continued e g the development of chatterbots with Racter and Jabberwacky An important development that eventually led to the statistical turn in the 1990s was the rising importance of quantitative evaluation in this period 8 Statistical NLP 1990s present edit Up until the 1980s most natural language processing systems were based on complex sets of hand written rules Starting in the late 1980s however there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing This was due to both the steady increase in computational power see Moore s law and the gradual lessening of the dominance of Chomskyan theories of linguistics e g transformational grammar whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine learning approach to language processing 9 1990s Many of the notable early successes in statistical methods in NLP occurred in the field of machine translation due especially to work at IBM Research such as IBM alignment models These systems were able to take advantage of existing multilingual textual corpora that had been produced by the Parliament of Canada and the European Union as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government However most other systems depended on corpora specifically developed for the tasks implemented by these systems which was and often continues to be a major limitation in the success of these systems As a result a great deal of research has gone into methods of more effectively learning from limited amounts of data 2000s With the growth of the web increasing amounts of raw unannotated language data have become available since the mid 1990s Research has thus increasingly focused on unsupervised and semi supervised learning algorithms Such algorithms can learn from data that has not been hand annotated with the desired answers or using a combination of annotated and non annotated data Generally this task is much more difficult than supervised learning and typically produces less accurate results for a given amount of input data However there is an enormous amount of non annotated data available including among other things the entire content of the World Wide Web which can often make up for the worse efficiency if the algorithm used has a low enough time complexity to be practical 2003 word n gram model at the time the best statistical algorithm is outperformed by a multi layer perceptron with a single hidden layer and context length of several words trained on up to 14 million words by Bengio et al 10 2010 Tom Mikolov then a PhD student at Brno University of Technology with co authors applied a simple recurrent neural network with a single hidden layer to language modelling 11 and in the following years he went on to develop Word2vec In the 2010s representation learning and deep neural network style featuring many hidden layers machine learning methods became widespread in natural language processing That popularity was due partly to a flurry of results showing that such techniques 12 13 can achieve state of the art results in many natural language tasks e g in language modeling 14 and parsing 15 16 This is increasingly important in medicine and healthcare where NLP helps analyze notes and text in electronic health records that would otherwise be inaccessible for study when seeking to improve care 17 or protect patient privacy 18 Approaches Symbolic statistical neural networks edit Symbolic approach i e the hand coding of a set of rules for manipulating symbols coupled with a dictionary lookup was historically the first approach used both by AI in general and by NLP in particular 19 20 such as by writing grammars or devising heuristic rules for stemming Machine learning approaches which include both statistical and neural networks on the other hand have many advantages over the symbolic approach both statistical and neural networks methods can focus more on the most common cases extracted from a corpus of texts whereas the rule based approach needs to provide rules for both rare cases and common ones equally language models produced by either statistical or neural networks methods are more robust to both unfamiliar e g containing words or structures that have not been seen before and erroneous input e g with misspelled words or words accidentally omitted in comparison to the rule based systems which are also more costly to produce the larger such a probabilistic language model is the more accurate it becomes in contrast to rule based systems that can gain accuracy only by increasing the amount and complexity of the rules leading to intractability problems Rule based systems are commonly used when the amount of training data is insufficient to successfully apply machine learning methods e g for the machine translation of low resource languages such as provided by the Apertium system for preprocessing in NLP pipelines e g tokenization or for postprocessing and transforming the output of NLP pipelines e g for knowledge extraction from syntactic parses Statistical approach edit In the late 1980s and mid 1990s the statistical approach ended a period of AI winter which was caused by the inefficiencies of the rule based approaches 21 22 The earliest decision trees producing systems of hard if then rules were still very similar to the old rule based approaches Only the introduction of hidden Markov models applied to part of speech tagging announced the end of the old rule based approach Neural networks edit Further information Artificial neural network A major drawback of statistical methods is that they require elaborate feature engineering Since 2015 23 the statistical approach has been replaced by the neural networks approach using semantic networks 24 and word embeddings to capture semantic properties of words Intermediate tasks e g part of speech tagging and dependency parsing are not needed anymore Neural machine translation based on then newly invented sequence to sequence transformations made obsolete the intermediate steps such as word alignment previously necessary for statistical machine translation Common NLP tasks edit The following is a list of some of the most commonly researched tasks in natural language processing Some of these tasks have direct real world applications while others more commonly serve as subtasks that are used to aid in solving larger tasks Though natural language processing tasks are closely intertwined they can be subdivided into categories for convenience A coarse division is given below Text and speech processing edit Optical character recognition OCR Given an image representing printed text determine the corresponding text Speech recognition Given a sound clip of a person or people speaking determine the textual representation of the speech This is the opposite of text to speech and is one of the extremely difficult problems colloquially termed AI complete see above In natural speech there are hardly any pauses between successive words and thus speech segmentation is a necessary subtask of speech recognition see below In most spoken languages the sounds representing successive letters blend into each other in a process termed coarticulation so the conversion of the analog signal to discrete characters can be a very difficult process Also given that words in the same language are spoken by people with different accents the speech recognition software must be able to recognize the wide variety of input as being identical to each other in terms of its textual equivalent Speech segmentation Given a sound clip of a person or people speaking separate it into words A subtask of speech recognition and typically grouped with it Text to speech Given a text transform those units and produce a spoken representation Text to speech can be used to aid the visually impaired 25 Word segmentation Tokenization Tokenization is a process used in text analysis that divides text into individual words or word fragments This technique results in two key components a word index and tokenized text The word index is a list that maps unique words to specific numerical identifiers and the tokenized text replaces each word with its corresponding numerical token These numerical tokens are then used in various deep learning methods 26 For a language like English this is fairly trivial since words are usually separated by spaces However some written languages like Chinese Japanese and Thai do not mark word boundaries in such a fashion and in those languages text segmentation is a significant task requiring knowledge of the vocabulary and morphology of words in the language Sometimes this process is also used in cases like bag of words BOW creation in data mining citation needed Morphological analysis edit Lemmatization The task of removing inflectional endings only and to return the base dictionary form of a word which is also known as a lemma Lemmatization is another technique for reducing words to their normalized form But in this case the transformation actually uses a dictionary to map words to their actual form 27 Morphological segmentation Separate words into individual morphemes and identify the class of the morphemes The difficulty of this task depends greatly on the complexity of the morphology i e the structure of words of the language being considered English has fairly simple morphology especially inflectional morphology and thus it is often possible to ignore this task entirely and simply model all possible forms of a word e g open opens opened opening as separate words In languages such as Turkish or Meitei a highly agglutinated Indian language however such an approach is not possible as each dictionary entry has thousands of possible word forms 28 Part of speech tagging Given a sentence determine the part of speech POS for each word Many words especially common ones can serve as multiple parts of speech For example book can be a noun the book on the table or verb to book a flight set can be a noun verb or adjective and out can be any of at least five different parts of speech Stemming The process of reducing inflected or sometimes derived words to a base form e g close will be the root for closed closing close closer etc Stemming yields similar results as lemmatization but does so on grounds of rules not a dictionary Syntactic analysis edit Part of a series onFormal languages Key concepts Formal system Alphabet Syntax Formal semantics Semantics programming languages Formal grammar Formation rule Well formed formula Automata theory Regular expression Production Ground expression Atomic formula Applications Formal methods Propositional calculus Predicate logic Mathematical notation Natural language processing Programming language theory Mathematical linguistics Computational linguistics Syntax analysis Formal verification Automated theorem proving vte Grammar induction 29 Generate a formal grammar that describes a language s syntax Sentence breaking also known as sentence boundary disambiguation Given a chunk of text find the sentence boundaries Sentence boundaries are often marked by periods or other punctuation marks but these same characters can serve other purposes e g marking abbreviations Parsing Determine the parse tree grammatical analysis of a given sentence The grammar for natural languages is ambiguous and typical sentences have multiple possible analyses perhaps surprisingly for a typical sentence there may be thousands of potential parses most of which will seem completely nonsensical to a human There are two primary types of parsing dependency parsing and constituency parsing Dependency parsing focuses on the relationships between words in a sentence marking things like primary objects and predicates whereas constituency parsing focuses on building out the parse tree using a probabilistic context free grammar PCFG see also stochastic grammar Lexical semantics of individual words in context edit Lexical semantics What is the computational meaning of individual words in context Distributional semantics How can we learn semantic representations from data Named entity recognition NER Given a stream of text determine which items in the text map to proper names such as people or places and what the type of each such name is e g person location organization Although capitalization can aid in recognizing named entities in languages such as English this information cannot aid in determining the type of named entity and in any case is often inaccurate or insufficient For example the first letter of a sentence is also capitalized and named entities often span several words only some of which are capitalized Furthermore many other languages in non Western scripts e g Chinese or Arabic do not have any capitalization at all and even languages with capitalization may not consistently use it to distinguish names For example German capitalizes all nouns regardless of whether they are names and French and Spanish do not capitalize names that serve as adjectives Another name for this task is token classification 30 Sentiment analysis see also Multimodal sentiment analysis Sentiment analysis is a computational method used to identify and classify the emotional intent behind text This technique involves analyzing text to determine whether the expressed sentiment is positive negative or neutral Models for sentiment classification typically utilize inputs such as word n grams Term Frequency Inverse Document Frequency TF IDF features hand generated features or employ deep learning models designed to recognize both long term and short term dependencies in text sequences The applications of sentiment analysis are diverse extending to tasks such as categorizing customer reviews on various online platforms 26 Terminology extraction The goal of terminology extraction is to automatically extract relevant terms from a given corpus Word sense disambiguation WSD Many words have more than one meaning we have to select the meaning which makes the most sense in context For this problem we are typically given a list of words and associated word senses e g from a dictionary or an online resource such as WordNet Entity linking Many words typically proper names refer to named entities here we have to select the entity a famous individual a location a company etc which is referred to in context Relational semantics semantics of individual sentences edit Relationship extraction Given a chunk of text identify the relationships among named entities e g who is married to whom Semantic parsing Given a piece of text typically a sentence produce a formal representation of its semantics either as a graph e g in AMR parsing or in accordance with a logical formalism e g in DRT parsing This challenge typically includes aspects of several more elementary NLP tasks from semantics e g semantic role labelling word sense disambiguation and can be extended to include full fledged discourse analysis e g discourse analysis coreference see Natural language understanding below Semantic role labelling see also implicit semantic role labelling below Given a single sentence identify and disambiguate semantic predicates e g verbal frames then identify and classify the frame elements semantic roles Discourse semantics beyond individual sentences edit Coreference resolution Given a sentence or larger chunk of text determine which words mentions refer to the same objects entities Anaphora resolution is a specific example of this task and is specifically concerned with matching up pronouns with the nouns or names to which they refer The more general task of coreference resolution also includes identifying so called bridging relationships involving referring expressions For example in a sentence such as He entered John s house through the front door the front door is a referring expression and the bridging relationship to be identified is the fact that the door being referred to is the front door of John s house rather than of some other structure that might also be referred to Discourse analysis This rubric includes several related tasks One task is discourse parsing i e identifying the discourse structure of a connected text i e the nature of the discourse relationships between sentences e g elaboration explanation contrast Another possible task is recognizing and classifying the speech acts in a chunk of text e g yes no question content question statement assertion etc Implicit semantic role labelling Given a single sentence identify and disambiguate semantic predicates e g verbal frames and their explicit semantic roles in the current sentence see Semantic role labelling above Then identify semantic roles that are not explicitly realized in the current sentence classify them into arguments that are explicitly realized elsewhere in the text and those that are not specified and resolve the former against the local text A closely related task is zero anaphora resolution i e the extension of coreference resolution to pro drop languages Recognizing textual entailment Given two text fragments determine if one being true entails the other entails the other s negation or allows the other to be either true or false 31 Topic segmentation and recognition Given a chunk of text separate it into segments each of which is devoted to a topic and identify the topic of the segment Argument mining The goal of argument mining is the automatic extraction and identification of argumentative structures from natural language text with the aid of computer programs 32 Such argumentative structures include the premise conclusions the argument scheme and the relationship between the main and subsidiary argument or the main and counter argument within discourse 33 34 Higher level NLP applications edit Automatic summarization text summarization Produce a readable summary of a chunk of text Often used to provide summaries of the text of a known type such as research papers articles in the financial section of a newspaper Grammatical error correction Grammatical error detection and correction involves a great band width of problems on all levels of linguistic analysis phonology orthography morphology syntax semantics pragmatics Grammatical error correction is impactful since it affects hundreds of millions of people that use or acquire English as a second language It has thus been subject to a number of shared tasks since 2011 35 36 37 As far as orthography morphology syntax and certain aspects of semantics are concerned and due to the development of powerful neural language models such as GPT 2 this can now 2019 be considered a largely solved problem and is being marketed in various commercial applications Logic translation Translate a text from a natural language into formal logic Machine translation MT Automatically translate text from one human language to another This is one of the most difficult problems and is a member of a class of problems colloquially termed AI complete i e requiring all of the different types of knowledge that humans possess grammar semantics facts about the real world etc to solve properly Natural language understanding NLU Convert chunks of text into more formal representations such as first order logic structures that are easier for computer programs to manipulate Natural language understanding involves the identification of the intended semantic from the multiple possible semantics which can be derived from a natural language expression which usually takes the form of organized notations of natural language concepts Introduction and creation of language metamodel and ontology are efficient however empirical solutions An explicit formalization of natural language semantics without confusions with implicit assumptions such as closed world assumption CWA vs open world assumption or subjective Yes No vs objective True False is expected for the construction of a basis of semantics formalization 38 Natural language generation NLG Convert information from computer databases or semantic intents into readable human language Book generation Not an NLP task proper but an extension of natural language generation and other NLP tasks is the creation of full fledged books The first machine generated book was created by a rule based system in 1984 Racter The policeman s beard is half constructed 39 The first published work by a neural network was published in 2018 1 the Road marketed as a novel contains sixty million words Both these systems are basically elaborate but non sensical semantics free language models The first machine generated science book was published in 2019 Beta Writer Lithium Ion Batteries Springer Cham 40 Unlike Racter and 1 the Road this is grounded on factual knowledge and based on text summarization Document AI A Document AI platform sits on top of the NLP technology enabling users with no prior experience of artificial intelligence machine learning or NLP to quickly train a computer to extract the specific data they need from different document types NLP powered Document AI enables non technical teams to quickly access information hidden in documents for example lawyers business analysts and accountants 41 Dialogue management Computer systems intended to converse with a human Question answering Given a human language question determine its answer Typical questions have a specific right answer such as What is the capital of Canada but sometimes open ended questions are also considered such as What is the meaning of life Text to image generation Given a description of an image generate an image that matches the description 42 Text to scene generation Given a description of a scene generate a 3D model of the scene 43 44 Text to video Given a description of a video generate a video that matches the description 45 46 General tendencies and possible future directions edit Based on long standing trends in the field it is possible to extrapolate future directions of NLP As of 2020 three trends among the topics of the long standing series of CoNLL Shared Tasks can be observed 47 Interest on increasingly abstract cognitive aspects of natural language 1999 2001 shallow parsing 2002 03 named entity recognition 2006 09 2017 18 dependency syntax 2004 05 2008 09 semantic role labelling 2011 12 coreference 2015 16 discourse parsing 2019 semantic parsing Increasing interest in multilinguality and potentially multimodality English since 1999 Spanish Dutch since 2002 German since 2003 Bulgarian Danish Japanese Portuguese Slovenian Swedish Turkish since 2006 Basque Catalan Chinese Greek Hungarian Italian Turkish since 2007 Czech since 2009 Arabic since 2012 2017 40 languages 2018 60 100 languages Elimination of symbolic representations rule based over supervised towards weakly supervised methods representation learning and end to end systems Cognition edit Most higher level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language More broadly speaking the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of NLP see trends among CoNLL shared tasks above Cognition refers to the mental action or process of acquiring knowledge and understanding through thought experience and the senses 48 Cognitive science is the interdisciplinary scientific study of the mind and its processes 49 Cognitive linguistics is an interdisciplinary branch of linguistics combining knowledge and research from both psychology and linguistics 50 Especially during the age of symbolic NLP the area of computational linguistics maintained strong ties with cognitive studies As an example George Lakoff offers a methodology to build natural language processing NLP algorithms through the perspective of cognitive science along with the findings of cognitive linguistics 51 with two defining aspects Apply the theory of conceptual metaphor explained by Lakoff as the understanding of one idea in terms of another which provides an idea of the intent of the author 52 For example consider the English word big When used in a comparison That is a big tree the author s intent is to imply that the tree is physically large relative to other trees or the authors experience When used metaphorically Tomorrow is a big day the author s intent to imply importance The intent behind other usages like in She is a big person will remain somewhat ambiguous to a person and a cognitive NLP algorithm alike without additional information Assign relative measures of meaning to a word phrase sentence or piece of text based on the information presented before and after the piece of text being analyzed e g by means of a probabilistic context free grammar PCFG The mathematical equation for such algorithms is presented in US Patent 9269353 53 R M M t o k e n N P M M t o k e n N 1 2 d i d d P M M t o k e n N P F t o k e n N i t o k e n N t o k e n N i i displaystyle RMM token N PMM token N times frac 1 2d left sum i d d PMM token N times PF token N i token N token N i i right Where RMM is the relative measure of meaning token is any block of text sentence phrase or word N is the number of tokens being analyzed PMM is the probable measure of meaning based on a corpora d is the non zero location of the token along the sequence of N tokens PF is the probability function specific to a language Ties with cognitive linguistics are part of the historical heritage of NLP but they have been less frequently addressed since the statistical turn during the 1990s Nevertheless approaches to develop cognitive models towards technically operationalizable frameworks have been pursued in the context of various frameworks e g of cognitive grammar 54 functional grammar 55 construction grammar 56 computational psycholinguistics and cognitive neuroscience e g ACT R however with limited uptake in mainstream NLP as measured by presence on major conferences 57 of the ACL More recently ideas of cognitive NLP have been revived as an approach to achieve explainability e g under the notion of cognitive AI 58 Likewise ideas of cognitive NLP are inherent to neural models multimodal NLP although rarely made explicit 59 and developments in artificial intelligence specifically tools and technologies using large language model approaches 60 and new directions in artificial general intelligence based on the free energy principle 61 by British neuroscientist and theoretician at University College London Karl J Friston See also edit 1 the Road Artificial intelligence detection software Automated essay scoring Biomedical text mining Compound term processing Computational linguistics Computer assisted reviewing Controlled natural language Deep learning Deep linguistic processing Distributional semantics Foreign language reading aid Foreign language writing aid Information extraction Information retrieval Language and Communication Technologies Language model Language technology Latent semantic indexing Multi agent system Native language identification Natural language programming Natural language understanding Natural language search Outline of natural language processing Query expansion Query understanding Reification linguistics Speech processing Spoken dialogue systems Text proofing Text simplification Transformer machine learning model Truecasing Question answering Word2vec References edit Eisenstein Jacob October 1 2019 Introduction to Natural Language Processing The MIT Press p 1 ISBN 9780262042840 NLP Hutchins J 2005 The history of machine translation in a nutshell PDF self published source ALPAC the in famous report John Hutchins MT News International no 14 June 1996 pp 9 12 Crevier 1993 pp 146 148 harvnb error no target CITEREFCrevier1993 help see also Buchanan 2005 p 56 harvnb error no target CITEREFBuchanan2005 help Early programs were necessarily limited in scope by the size and speed of memory Koskenniemi Kimmo 1983 Two level morphology A general computational model of word form recognition and production PDF Department of General Linguistics University of Helsinki Joshi A K Weinstein S 1981 August Control of Inference Role of Some Aspects of Discourse Structure Centering In IJCAI pp 385 387 Guida G Mauri G July 1986 Evaluation of natural language processing systems Issues and approaches Proceedings of the IEEE 74 7 1026 1035 doi 10 1109 PROC 1986 13580 ISSN 1558 2256 S2CID 30688575 Chomskyan linguistics encourages the investigation of corner cases that stress the limits of its theoretical models comparable to pathological phenomena in mathematics typically created using thought experiments rather than the systematic investigation of typical phenomena that occur in real world data as is the case in corpus linguistics The creation and use of such corpora of real world data is a fundamental part of machine learning algorithms for natural language processing In addition theoretical underpinnings of Chomskyan linguistics such as the so called poverty of the stimulus argument entail that general learning algorithms as are typically used in machine learning cannot be successful in language processing As a 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2022 11 02 Retrieved 2022 11 09 Vincent James 2022 09 29 Meta s new text to video AI generator is like DALL E for video The Verge Retrieved 2022 11 09 Previous shared tasks CoNLL www conll org Retrieved 2021 01 11 Cognition Lexico Oxford University Press and Dictionary com Archived from the original on July 15 2020 Retrieved 6 May 2020 Ask the Cognitive Scientist American Federation of Teachers 8 August 2014 Cognitive science is an interdisciplinary field of researchers from Linguistics psychology neuroscience philosophy computer science and anthropology that seek to understand the mind Robinson Peter 2008 Handbook of Cognitive Linguistics and Second Language Acquisition Routledge pp 3 8 ISBN 978 0 805 85352 0 Lakoff George 1999 Philosophy in the Flesh The Embodied Mind and Its Challenge to Western Philosophy Appendix The Neural Theory of Language Paradigm New York Basic Books pp 569 583 ISBN 978 0 465 05674 3 Strauss Claudia 1999 A Cognitive Theory of Cultural Meaning Cambridge University Press pp 156 164 ISBN 978 0 521 59541 4 US patent 9269353 Universal Conceptual Cognitive Annotation UCCA Universal Conceptual Cognitive Annotation UCCA Retrieved 2021 01 11 Rodr guez F C Mairal Us n R 2016 Building an RRG computational grammar Onomazein 34 86 117 Fluid Construction Grammar A fully operational processing system for construction grammars Retrieved 2021 01 11 ACL Member Portal The Association for Computational Linguistics Member Portal www aclweb org Retrieved 2021 01 11 Chunks and Rules W3C Retrieved 2021 01 11 Socher Richard Karpathy Andrej Le Quoc V Manning Christopher D Ng Andrew Y 2014 Grounded Compositional Semantics for Finding and Describing Images with Sentences Transactions of the Association for Computational Linguistics 2 207 218 doi 10 1162 tacl a 00177 S2CID 2317858 Dasgupta Ishita Lampinen Andrew K Chan Stephanie C Y Creswell Antonia Kumaran Dharshan McClelland James L Hill Felix 2022 Language models show human like content effects on reasoning Dasgupta Lampinen et al arXiv 2207 07051 cs CL Friston Karl J 2022 Active Inference The Free Energy Principle in Mind Brain and Behavior Chapter 4 The Generative Models of Active Inference The MIT Press ISBN 978 0 262 36997 8 Further reading edit Bates M 1995 Models of natural language understanding Proceedings of the National Academy of Sciences of the United States of America 92 22 9977 9982 Bibcode 1995PNAS 92 9977B doi 10 1073 pnas 92 22 9977 PMC 40721 PMID 7479812 Steven Bird Ewan Klein and Edward Loper 2009 Natural Language Processing with Python O Reilly Media ISBN 978 0 596 51649 9 Kenna Hughes Castleberry A Murder Mystery Puzzle The literary puzzle Cain s Jawbone which has stumped humans for decades reveals the limitations of natural language processing algorithms Scientific American vol 329 no 4 November 2023 pp 81 82 This murder mystery competition has revealed that although NLP natural language processing models are capable of incredible feats their abilities are very much limited by the amount of context they receive This could cause difficulties for researchers who hope to use them to do things such as analyze ancient languages In some cases there are few historical records on long gone civilizations to serve as training data for such a purpose p 82 Daniel Jurafsky and James H Martin 2008 Speech and Language Processing 2nd edition Pearson Prentice Hall ISBN 978 0 13 187321 6 Mohamed Zakaria Kurdi 2016 Natural Language Processing and Computational Linguistics speech morphology and syntax Volume 1 ISTE Wiley ISBN 978 1848218482 Mohamed Zakaria Kurdi 2017 Natural Language Processing and Computational Linguistics semantics discourse and applications Volume 2 ISTE Wiley ISBN 978 1848219212 Christopher D Manning Prabhakar Raghavan and Hinrich Sch tze 2008 Introduction to Information Retrieval Cambridge University Press ISBN 978 0 521 86571 5 Official html and pdf versions available without charge Christopher D Manning and Hinrich Sch tze 1999 Foundations of Statistical Natural Language Processing The MIT Press ISBN 978 0 262 13360 9 David M W Powers and Christopher C R Turk 1989 Machine Learning of Natural Language Springer Verlag ISBN 978 0 387 19557 5 External links edit Media related to Natural language processing at Wikimedia Commons vteNatural language processingGeneral terms AI complete Bag of words n gram Bigram Trigram Computational linguistics Natural language understanding Stop words Text processing Text analysis Argument mining Collocation extraction Concept mining Coreference resolution Deep linguistic processing Distant reading Information extraction Named entity recognition Ontology learning Parsing Semantic parsing Syntactic parsing Part of speech tagging Semantic analysis Semantic role labeling Semantic decomposition Semantic similarity Sentiment analysis Terminology extraction Text mining Textual entailment Truecasing Word sense disambiguation Word sense induction Text segmentation Compound term processing Lemmatisation Lexical analysis Text chunking Stemming Sentence segmentation Word segmentation Automatic summarization Multi document summarization Sentence extraction Text simplification Machine translation Computer assisted Example based Rule based Statistical Transfer based Neural Distributional semantics models BERT Document term matrix Explicit semantic analysis fastText GloVe Language model large Latent semantic analysis Seq2seq Word embedding Word2vec Language resources datasets and corporaTypes andstandards Corpus linguistics Lexical resource Linguistic Linked Open Data Machine readable dictionary Parallel text PropBank Semantic network Simple Knowledge Organization System Speech corpus Text corpus Thesaurus information retrieval Treebank Universal Dependencies Data BabelNet Bank of English DBpedia FrameNet Google Ngram Viewer UBY WordNet Wikidata Automatic identificationand data capture Speech recognition Speech segmentation Speech synthesis Natural language generation Optical character recognition Topic model Document classification Latent Dirichlet allocation Pachinko allocation Computer assistedreviewing Automated essay scoring Concordancer Grammar checker Predictive text Pronunciation assessment Spell checker Natural languageuser interface Chatbot Interactive fiction Question answering Virtual assistant Voice user interface Related Formal semantics Hallucination Natural Language Toolkit spaCy Portal Language Authority control databases NationalUnited StatesJapanCzech RepublicIsraelOtherYale LUX Retrieved from https en wikipedia org w index php title Natural language processing oldid 1301380737 Categories Natural language processingComputational fields of studyComputational linguisticsSpeech recognitionHidden categories All accuracy disputesAccuracy disputes from December 2013Harv and Sfn no target errorsCS1 errors periodical ignoredCS1 maint locationArticles with short descriptionShort description is different from WikidataArticles needing additional references 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text_lowercase: str = text_without_whitespace.lower()print(text_lowercase)natural language processing wikipedia jump to content main menu main menu move to sidebar hide navigation main pagecontentscurrent eventsrandom articleabout wikipediacontact us contribute helplearn to editcommunity portalrecent changesupload filespecial pages search search appearance donate create account log in personal tools donate create account log in pages for logged out editors learn more contributionstalk contents move to sidebar hide top 1 history toggle history subsection 1 1 symbolic nlp 1950s early 1990s 1 2 statistical nlp 1990s present 2 approaches symbolic statistical neural networks toggle approaches symbolic statistical neural networks subsection 2 1 statistical approach 2 2 neural networks 3 common nlp tasks toggle common nlp tasks subsection 3 1 text and speech processing 3 2 morphological analysis 3 3 syntactic analysis 3 4 lexical semantics of individual words in context 3 5 relational semantics semantics of individual sentences 3 6 discourse semantics beyond individual sentences 3 7 higher level nlp applications 4 general tendencies and possible future directions toggle general tendencies and possible future directions subsection 4 1 cognition 5 see also 6 references 7 further reading 8 external links toggle the table of contents natural language processing 71 languages afrikaans az rbaycanca b n l m g bosanskibrezhonegcatal e tinacymraegdanskdeutscheesti espa olesperantoeuskara fran aisgaeilgegalego hrvatskiidobahasa indonesiaisizulu slenskaitaliano latvie ulietuvi nederlands norsk bokm l picardpiemont ispolskiportugu sqaraqalpaqsharom n runa simi shqipsimple english srpskisrpskohrvatski suomi t rk e ti ng vi t edit links articletalk english readeditview history tools tools move to sidebar hide actions readeditview history general what links hererelated changesupload filepermanent linkpage informationcite this pageget shortened urldownload qr code print export 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to remove this message learn how and when to remove this message natural language processing nlp is the processing of natural language information by a computer the study of nlp a subfield of computer science is generally associated with artificial intelligence nlp is related to information retrieval knowledge representation computational linguistics and more broadly with linguistics 1 major processing tasks in an nlp system include speech recognition text classification natural language understanding and natural language generation history edit further information history of natural language processing natural language processing has its roots in the 1950s 2 already in 1950 alan turing published an article titled computing machinery and intelligence which proposed what is now called the turing test as a criterion of intelligence though at the time that was not articulated as a problem separate from artificial intelligence the proposed test includes a task that involves the automated interpretation and generation of natural language symbolic nlp 1950s early 1990s edit the premise of symbolic nlp is well summarized by john searle s chinese room experiment given a collection of rules e g a chinese phrasebook with questions and matching answers the computer emulates natural language understanding or other nlp tasks by applying those rules to the data it confronts 1950s the georgetown experiment in 1954 involved fully automatic translation of more than sixty russian sentences into english the authors claimed that within three or five years machine translation would be a solved problem 3 however real progress was much slower and after the alpac report in 1966 which found that ten years of research had failed to fulfill the expectations funding for machine translation was dramatically reduced little further research in machine translation was conducted in america though some research continued elsewhere such as japan and europe 4 until the late 1980s when the first statistical machine translation systems were developed 1960s some notably successful natural language processing systems developed in the 1960s were shrdlu a natural language system working in restricted blocks worlds with restricted vocabularies and eliza a simulation of a rogerian psychotherapist written by joseph weizenbaum between 1964 and 1966 using almost no information about human thought or emotion eliza sometimes provided a startlingly human like interaction when the patient exceeded the very small knowledge base eliza might provide a generic response for example responding to my head hurts with why do you say your head hurts ross quillian s successful work on natural language was demonstrated with a vocabulary of only twenty words because that was all that would fit in a computer memory at the time 5 1970s during the 1970s many programmers began to write conceptual ontologies which structured real world information into computer understandable data examples are margie schank 1975 sam cullingford 1978 pam wilensky 1978 talespin meehan 1976 qualm lehnert 1977 politics carbonell 1979 and plot units lehnert 1981 during this time the first chatterbots were written e g parry 1980s the 1980s and early 1990s mark the heyday of symbolic methods in nlp focus areas of the time included research on rule based parsing e g the development of hpsg as a computational operationalization of generative grammar morphology e g two level morphology 6 semantics e g lesk algorithm reference e g within centering theory 7 and other areas of natural language understanding e g in the rhetorical structure theory other lines of research were continued e g the development of chatterbots with racter and jabberwacky an important development that eventually led to the statistical turn in the 1990s was the rising importance of quantitative evaluation in this period 8 statistical nlp 1990s present edit up until the 1980s most natural language processing systems were based on complex sets of hand written rules starting in the late 1980s however there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing this was due to both the steady increase in computational power see moore s law and the gradual lessening of the dominance of chomskyan theories of linguistics e g transformational grammar whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine learning approach to language processing 9 1990s many of the notable early successes in statistical methods in nlp occurred in the field of machine translation due especially to work at ibm research such as ibm alignment models these systems were able to take advantage of existing multilingual textual corpora that had been produced by the parliament of canada and the european union as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government however most other systems depended on corpora specifically developed for the tasks implemented by these systems which was and often continues to be a major limitation in the success of these systems as a result a great deal of research has gone into methods of more effectively learning from limited amounts of data 2000s with the growth of the web increasing amounts of raw unannotated language data have become available since the mid 1990s research has thus increasingly focused on unsupervised and semi supervised learning algorithms such algorithms can learn from data that has not been hand annotated with the desired answers or using a combination of annotated and non annotated data generally this task is much more difficult than supervised learning and typically produces less accurate results for a given amount of input data however there is an enormous amount of non annotated data available including among other things the entire content of the world wide web which can often make up for the worse efficiency if the algorithm used has a low enough time complexity to be practical 2003 word n gram model at the time the best statistical algorithm is outperformed by a multi layer perceptron with a single hidden layer and context length of several words trained on up to 14 million words by bengio et al 10 2010 tom mikolov then a phd student at brno university of technology with co authors applied a simple recurrent neural network with a single hidden layer to language modelling 11 and in the following years he went on to develop word2vec in the 2010s representation learning and deep neural network style featuring many hidden layers machine learning methods became widespread in natural language processing that popularity was due partly to a flurry of results showing that such techniques 12 13 can achieve state of the art results in many natural language tasks e g in language modeling 14 and parsing 15 16 this is increasingly important in medicine and healthcare where nlp helps analyze notes and text in electronic health records that would otherwise be inaccessible for study when seeking to improve care 17 or protect patient privacy 18 approaches symbolic statistical neural networks edit symbolic approach i e the hand coding of a set of rules for manipulating symbols coupled with a dictionary lookup was historically the first approach used both by ai in general and by nlp in particular 19 20 such as by writing grammars or devising heuristic rules for stemming machine learning approaches which include both statistical and neural networks on the other hand have many advantages over the symbolic approach both statistical and neural networks methods can focus more on the most common cases extracted from a corpus of texts whereas the rule based approach needs to provide rules for both rare cases and common ones equally language models produced by either statistical or neural networks methods are more robust to both unfamiliar e g containing words or structures that have not been seen before and erroneous input e g with misspelled words or words accidentally omitted in comparison to the rule based systems which are also more costly to produce the larger such a probabilistic language model is the more accurate it becomes in contrast to rule based systems that can gain accuracy only by increasing the amount and complexity of the rules leading to intractability problems rule based systems are commonly used when the amount of training data is insufficient to successfully apply machine learning methods e g for the machine translation of low resource languages such as provided by the apertium system for preprocessing in nlp pipelines e g tokenization or for postprocessing and transforming the output of nlp pipelines e g for knowledge extraction from syntactic parses statistical approach edit in the late 1980s and mid 1990s the statistical approach ended a period of ai winter which was caused by the inefficiencies of the rule based approaches 21 22 the earliest decision trees producing systems of hard if then rules were still very similar to the old rule based approaches only the introduction of hidden markov models applied to part of speech tagging announced the end of the old rule based approach neural networks edit further information artificial neural network a major drawback of statistical methods is that they require elaborate feature engineering since 2015 23 the statistical approach has been replaced by the neural networks approach using semantic networks 24 and word embeddings to capture semantic properties of words intermediate tasks e g part of speech tagging and dependency parsing are not needed anymore neural machine translation based on then newly invented sequence to sequence transformations made obsolete the intermediate steps such as word alignment previously necessary for statistical machine translation common nlp tasks edit the following is a list of some of the most commonly researched tasks in natural language processing some of these tasks have direct real world applications while others more commonly serve as subtasks that are used to aid in solving larger tasks though natural language processing tasks are closely intertwined they can be subdivided into categories for convenience a coarse division is given below text and speech processing edit optical character recognition ocr given an image representing printed text determine the corresponding text speech recognition given a sound clip of a person or people speaking determine the textual representation of the speech this is the opposite of text to speech and is one of the extremely difficult problems colloquially termed ai complete see above in natural speech there are hardly any pauses between successive words and thus speech segmentation is a necessary subtask of speech recognition see below in most spoken languages the sounds representing successive letters blend into each other in a process termed coarticulation so the conversion of the analog signal to discrete characters can be a very difficult process also given that words in the same language are spoken by people with different accents the speech recognition software must be able to recognize the wide variety of input as being identical to each other in terms of its textual equivalent speech segmentation given a sound clip of a person or people speaking separate it into words a subtask of speech recognition and typically grouped with it text to speech given a text transform those units and produce a spoken representation text to speech can be used to aid the visually impaired 25 word segmentation tokenization tokenization is a process used in text analysis that divides text into individual words or word fragments this technique results in two key components a word index and tokenized text the word index is a list that maps unique words to specific numerical identifiers and the tokenized text replaces each word with its corresponding numerical token these numerical tokens are then used in various deep learning methods 26 for a language like english this is fairly trivial since words are usually separated by spaces however some written languages like chinese japanese and thai do not mark word boundaries in such a fashion and in those languages text segmentation is a significant task requiring knowledge of the vocabulary and morphology of words in the language sometimes this process is also used in cases like bag of words bow creation in data mining citation needed morphological analysis edit lemmatization the task of removing inflectional endings only and to return the base dictionary form of a word which is also known as a lemma lemmatization is another technique for reducing words to their normalized form but in this case the transformation actually uses a dictionary to map words to their actual form 27 morphological segmentation separate words into individual morphemes and identify the class of the morphemes the difficulty of this task depends greatly on the complexity of the morphology i e the structure of words of the language being considered english has fairly simple morphology especially inflectional morphology and thus it is often possible to ignore this task entirely and simply model all possible forms of a word e g open opens opened opening as separate words in languages such as turkish or meitei a highly agglutinated indian language however such an approach is not possible as each dictionary entry has thousands of possible word forms 28 part of speech tagging given a sentence determine the part of speech pos for each word many words especially common ones can serve as multiple parts of speech for example book can be a noun the book on the table or verb to book a flight set can be a noun verb or adjective and out can be any of at least five different parts of speech stemming the process of reducing inflected or sometimes derived words to a base form e g close will be the root for closed closing close closer etc stemming yields similar results as lemmatization but does so on grounds of rules not a dictionary syntactic analysis edit part of a series onformal languages key concepts formal system alphabet syntax formal semantics semantics programming languages formal grammar formation rule well formed formula automata theory regular expression production ground expression atomic formula applications formal methods propositional calculus predicate logic mathematical notation natural language processing programming language theory mathematical linguistics computational linguistics syntax analysis formal verification automated theorem proving vte grammar induction 29 generate a formal grammar that describes a language s syntax sentence breaking also known as sentence boundary disambiguation given a chunk of text find the sentence boundaries sentence boundaries are often marked by periods or other punctuation marks but these same characters can serve other purposes e g marking abbreviations parsing determine the parse tree grammatical analysis of a given sentence the grammar for natural languages is ambiguous and typical sentences have multiple possible analyses perhaps surprisingly for a typical sentence there may be thousands of potential parses most of which will seem completely nonsensical to a human there are two primary types of parsing dependency parsing and constituency parsing dependency parsing focuses on the relationships between words in a sentence marking things like primary objects and predicates whereas constituency parsing focuses on building out the parse tree using a probabilistic context free grammar pcfg see also stochastic grammar lexical semantics of individual words in context edit lexical semantics what is the computational meaning of individual words in context distributional semantics how can we learn semantic representations from data named entity recognition ner given a stream of text determine which items in the text map to proper names such as people or places and what the type of each such name is e g person location organization although capitalization can aid in recognizing named entities in languages such as english this information cannot aid in determining the type of named entity and in any case is often inaccurate or insufficient for example the first letter of a sentence is also capitalized and named entities often span several words only some of which are capitalized furthermore many other languages in non western scripts e g chinese or arabic do not have any capitalization at all and even languages with capitalization may not consistently use it to distinguish names for example german capitalizes all nouns regardless of whether they are names and french and spanish do not capitalize names that serve as adjectives another name for this task is token classification 30 sentiment analysis see also multimodal sentiment analysis sentiment analysis is a computational method used to identify and classify the emotional intent behind text this technique involves analyzing text to determine whether the expressed sentiment is positive negative or neutral models for sentiment classification typically utilize inputs such as word n grams term frequency inverse document frequency tf idf features hand generated features or employ deep learning models designed to recognize both long term and short term dependencies in text sequences the applications of sentiment analysis are diverse extending to tasks such as categorizing customer reviews on various online platforms 26 terminology extraction the goal of terminology extraction is to automatically extract relevant terms from a given corpus word sense disambiguation wsd many words have more than one meaning we have to select the meaning which makes the most sense in context for this problem we are typically given a list of words and associated word senses e g from a dictionary or an online resource such as wordnet entity linking many words typically proper names refer to named entities here we have to select the entity a famous individual a location a company etc which is referred to in context relational semantics semantics of individual sentences edit relationship extraction given a chunk of text identify the relationships among named entities e g who is married to whom semantic parsing given a piece of text typically a sentence produce a formal representation of its semantics either as a graph e g in amr parsing or in accordance with a logical formalism e g in drt parsing this challenge typically includes aspects of several more elementary nlp tasks from semantics e g semantic role labelling word sense disambiguation and can be extended to include full fledged discourse analysis e g discourse analysis coreference see natural language understanding below semantic role labelling see also implicit semantic role labelling below given a single sentence identify and disambiguate semantic predicates e g verbal frames then identify and classify the frame elements semantic roles discourse semantics beyond individual sentences edit coreference resolution given a sentence or larger chunk of text determine which words mentions refer to the same objects entities anaphora resolution is a specific example of this task and is specifically concerned with matching up pronouns with the nouns or names to which they refer the more general task of coreference resolution also includes identifying so called bridging relationships involving referring expressions for example in a sentence such as he entered john s house through the front door the front door is a referring expression and the bridging relationship to be identified is the fact that the door being referred to is the front door of john s house rather than of some other structure that might also be referred to discourse analysis this rubric includes several related tasks one task is discourse parsing i e identifying the discourse structure of a connected text i e the nature of the discourse relationships between sentences e g elaboration explanation contrast another possible task is recognizing and classifying the speech acts in a chunk of text e g yes no question content question statement assertion etc implicit semantic role labelling given a single sentence identify and disambiguate semantic predicates e g verbal frames and their explicit semantic roles in the current sentence see semantic role labelling above then identify semantic roles that are not explicitly realized in the current sentence classify them into arguments that are explicitly realized elsewhere in the text and those that are not specified and resolve the former against the local text a closely related task is zero anaphora resolution i e the extension of coreference resolution to pro drop languages recognizing textual entailment given two text fragments determine if one being true entails the other entails the other s negation or allows the other to be either true or false 31 topic segmentation and recognition given a chunk of text separate it into segments each of which is devoted to a topic and identify the topic of the segment argument mining the goal of argument mining is the automatic extraction and identification of argumentative structures from natural language text with the aid of computer programs 32 such argumentative structures include the premise conclusions the argument scheme and the relationship between the main and subsidiary argument or the main and counter argument within discourse 33 34 higher level nlp applications edit automatic summarization text summarization produce a readable summary of a chunk of text often used to provide summaries of the text of a known type such as research papers articles in the financial section of a newspaper grammatical error correction grammatical error detection and correction involves a great band width of problems on all levels of linguistic analysis phonology orthography morphology syntax semantics pragmatics grammatical error correction is impactful since it affects hundreds of millions of people that use or acquire english as a second language it has thus been subject to a number of shared tasks since 2011 35 36 37 as far as orthography morphology syntax and certain aspects of semantics are concerned and due to the development of powerful neural language models such as gpt 2 this can now 2019 be considered a largely solved problem and is being marketed in various commercial applications logic translation translate a text from a natural language into formal logic machine translation mt automatically translate text from one human language to another this is one of the most difficult problems and is a member of a class of problems colloquially termed ai complete i e requiring all of the different types of knowledge that humans possess grammar semantics facts about the real world etc to solve properly natural language understanding nlu convert chunks of text into more formal representations such as first order logic structures that are easier for computer programs to manipulate natural language understanding involves the identification of the intended semantic from the multiple possible semantics which can be derived from a natural language expression which usually takes the form of organized notations of natural language concepts introduction and creation of language metamodel and ontology are efficient however empirical solutions an explicit formalization of natural language semantics without confusions with implicit assumptions such as closed world assumption cwa vs open world assumption or subjective yes no vs objective true false is expected for the construction of a basis of semantics formalization 38 natural language generation nlg convert information from computer databases or semantic intents into readable human language book generation not an nlp task proper but an extension of natural language generation and other nlp tasks is the creation of full fledged books the first machine generated book was created by a rule based system in 1984 racter the policeman s beard is half constructed 39 the first published work by a neural network was published in 2018 1 the road marketed as a novel contains sixty million words both these systems are basically elaborate but non sensical semantics free language models the first machine generated science book was published in 2019 beta writer lithium ion batteries springer cham 40 unlike racter and 1 the road this is grounded on factual knowledge and based on text summarization document ai a document ai platform sits on top of the nlp technology enabling users with no prior experience of artificial intelligence machine learning or nlp to quickly train a computer to extract the specific data they need from different document types nlp powered document ai enables non technical teams to quickly access information hidden in documents for example lawyers business analysts and accountants 41 dialogue management computer systems intended to converse with a human question answering given a human language question determine its answer typical questions have a specific right answer such as what is the capital of canada but sometimes open ended questions are also considered such as what is the meaning of life text to image generation given a description of an image generate an image that matches the description 42 text to scene generation given a description of a scene generate a 3d model of the scene 43 44 text to video given a description of a video generate a video that matches the description 45 46 general tendencies and possible future directions edit based on long standing trends in the field it is possible to extrapolate future directions of nlp as of 2020 three trends among the topics of the long standing series of conll shared tasks can be observed 47 interest on increasingly abstract cognitive aspects of natural language 1999 2001 shallow parsing 2002 03 named entity recognition 2006 09 2017 18 dependency syntax 2004 05 2008 09 semantic role labelling 2011 12 coreference 2015 16 discourse parsing 2019 semantic parsing increasing interest in multilinguality and potentially multimodality english since 1999 spanish dutch since 2002 german since 2003 bulgarian danish japanese portuguese slovenian swedish turkish since 2006 basque catalan chinese greek hungarian italian turkish since 2007 czech since 2009 arabic since 2012 2017 40 languages 2018 60 100 languages elimination of symbolic representations rule based over supervised towards weakly supervised methods representation learning and end to end systems cognition edit most higher level nlp applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language more broadly speaking the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of nlp see trends among conll shared tasks above cognition refers to the mental action or process of acquiring knowledge and understanding through thought experience and the senses 48 cognitive science is the interdisciplinary scientific study of the mind and its processes 49 cognitive linguistics is an interdisciplinary branch of linguistics combining knowledge and research from both psychology and linguistics 50 especially during the age of symbolic nlp the area of computational linguistics maintained strong ties with cognitive studies as an example george lakoff offers a methodology to build natural language processing nlp algorithms through the perspective of cognitive science along with the findings of cognitive linguistics 51 with two defining aspects apply the theory of conceptual metaphor explained by lakoff as the understanding of one idea in terms of another which provides an idea of the intent of the author 52 for example consider the english word big when used in a comparison that is a big tree the author s intent is to imply that the tree is physically large relative to other trees or the authors experience when used metaphorically tomorrow is a big day the author s intent to imply importance the intent behind other usages like in she is a big person will remain somewhat ambiguous to a person and a cognitive nlp algorithm alike without additional information assign relative measures of meaning to a word phrase sentence or piece of text based on the information presented before and after the piece of text being analyzed e g by means of a probabilistic context free grammar pcfg the mathematical equation for such algorithms is presented in us patent 9269353 53 r m m t o k e n n p m m t o k e n n 1 2 d i d d p m m t o k e n n p f t o k e n n i t o k e n n t o k e n n i i displaystyle rmm token n pmm token n times frac 1 2d left sum i d d pmm token n times pf token n i token n token n i i right where rmm is the relative measure of meaning token is any block of text sentence phrase or word n is the number of tokens being analyzed pmm is the probable measure of meaning based on a corpora d is the non zero location of the token along the sequence of n tokens pf is the probability function specific to a language ties with cognitive linguistics are part of the historical heritage of nlp but they have been less frequently addressed since the statistical turn during the 1990s nevertheless approaches to develop cognitive models towards technically operationalizable frameworks have been pursued in the context of various frameworks e g of cognitive grammar 54 functional grammar 55 construction grammar 56 computational psycholinguistics and cognitive neuroscience e g act r however with limited uptake in mainstream nlp as measured by presence on major conferences 57 of the acl more recently ideas of cognitive nlp have been revived as an approach to achieve explainability e g under the notion of cognitive ai 58 likewise ideas of cognitive nlp are inherent to neural models multimodal nlp although rarely made explicit 59 and developments in artificial intelligence specifically tools and technologies using large language model approaches 60 and new directions in artificial general intelligence based on the free energy principle 61 by british neuroscientist and theoretician at university college london karl j friston see also edit 1 the road artificial intelligence detection software automated essay scoring biomedical text mining compound term processing computational linguistics computer assisted reviewing controlled natural language deep learning deep linguistic processing distributional semantics foreign language reading aid foreign language writing aid information extraction information retrieval language and communication technologies language model language technology latent semantic indexing multi agent system native language identification natural language programming natural language understanding natural language search outline of natural language processing query expansion query understanding reification linguistics speech processing spoken dialogue systems text proofing text simplification transformer machine learning model truecasing question answering word2vec references edit eisenstein jacob october 1 2019 introduction to natural language processing the mit press p 1 isbn 9780262042840 nlp hutchins j 2005 the history of machine translation in a nutshell pdf self published source alpac the in famous report john hutchins mt news international no 14 june 1996 pp 9 12 crevier 1993 pp 146 148 harvnb error no target citerefcrevier1993 help see also buchanan 2005 p 56 harvnb error no target citerefbuchanan2005 help early programs were necessarily limited in scope by the size and speed of memory koskenniemi kimmo 1983 two level morphology a general computational model of word form recognition and production pdf department of general linguistics university of helsinki joshi a k weinstein s 1981 august control of inference role of some aspects of discourse structure centering in ijcai pp 385 387 guida g mauri g july 1986 evaluation of natural language processing systems issues and approaches proceedings of the ieee 74 7 1026 1035 doi 10 1109 proc 1986 13580 issn 1558 2256 s2cid 30688575 chomskyan linguistics encourages the investigation of corner cases that stress the limits of its theoretical models comparable to pathological phenomena in mathematics typically created using thought experiments rather than the systematic investigation of typical phenomena that occur in real world data as is the case in corpus linguistics the creation and use of such corpora of real world data is a fundamental part of machine learning algorithms for natural language processing in addition theoretical underpinnings of chomskyan linguistics such as the so called poverty of the stimulus argument entail that general learning algorithms as are typically used in machine learning cannot be successful in language processing as a result the chomskyan paradigm discouraged the application of such models to language processing bengio yoshua ducharme r jean vincent pascal janvin christian march 1 2003 a neural probabilistic language model the journal of machine learning research 3 1137 1155 via acm digital library mikolov tom karafi t martin burget luk ernock jan khudanpur sanjeev 26 september 2010 recurrent neural network based language model pdf interspeech 2010 pp 1045 1048 doi 10 21437 interspeech 2010 343 s2cid 17048224 cite book journal ignored help goldberg yoav 2016 a primer on neural network models for natural language processing journal of artificial intelligence research 57 345 420 arxiv 1807 10854 doi 10 1613 jair 4992 s2cid 8273530 goodfellow ian bengio yoshua courville aaron 2016 deep learning mit press jozefowicz rafal vinyals oriol schuster mike shazeer noam wu yonghui 2016 exploring the limits of language modeling arxiv 1602 02410 bibcode 2016arxiv160202410j choe do kook charniak eugene parsing as language modeling emnlp 2016 archived from the original on 2018 10 23 retrieved 2018 10 22 vinyals oriol et al 2014 grammar as a foreign language pdf nips2015 arxiv 1412 7449 bibcode 2014arxiv1412 7449v turchin alexander florez builes luisa f 2021 03 19 using natural language processing to measure and improve quality of diabetes care a systematic review journal of diabetes science and technology 15 3 553 560 doi 10 1177 19322968211000831 issn 1932 2968 pmc 8120048 pmid 33736486 lee jennifer yang samuel holland hall cynthia sezgin emre gill manjot linwood simon huang yungui hoffman jeffrey 2022 06 10 prevalence of sensitive terms in clinical notes using natural language processing techniques observational study jmir medical informatics 10 6 e38482 doi 10 2196 38482 issn 2291 9694 pmc 9233261 pmid 35687381 winograd terry 1971 procedures as a representation for data in a computer program for understanding natural language thesis schank roger c abelson robert p 1977 scripts plans goals and understanding an inquiry into human knowledge structures hillsdale erlbaum isbn 0 470 99033 3 mark johnson how the statistical revolution changes computational linguistics proceedings of the eacl 2009 workshop on the interaction between linguistics and computational linguistics philip resnik four revolutions language log february 5 2011 socher richard deep learning for nlp acl 2012 tutorial www socher org retrieved 2020 08 17 this was an early deep learning tutorial at the acl 2012 and met with both interest and at the time skepticism by most participants until then neural learning was basically rejected because of its lack of statistical interpretability until 2015 deep learning had evolved into the major framework of nlp link is broken try http web stanford edu class cs224n segev elad 2022 semantic network analysis in social sciences london routledge isbn 9780367636524 archived from the original on 5 december 2021 retrieved 5 december 2021 yi chucai tian yingli 2012 assistive text reading from complex background for blind persons camera based document analysis and recognition lecture notes in computer science vol 7139 springer berlin heidelberg pp 15 28 citeseerx 10 1 1 668 869 doi 10 1007 978 3 642 29364 1 2 isbn 9783642293634 a b natural language processing nlp a complete guide www deeplearning ai 2023 01 11 retrieved 2024 05 05 what is natural language processing intro to nlp in machine learning gyansetu 2020 12 06 retrieved 2021 01 09 kishorjit n vidya raj rk nirmal y sivaji b 2012 manipuri morpheme identification pdf proceedings of the 3rd workshop on south and southeast asian natural language processing sanlp coling 2012 mumbai december 2012 95 108 cite journal cs1 maint location link klein dan manning christopher d 2002 natural language grammar induction using a constituent context model pdf advances in neural information processing systems kariampuzha william alyea gioconda qu sue sanjak jaleal math ewy sid eric chatelaine haley yadaw arjun xu yanji zhu 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2022 11 02 retrieved 2022 11 09 vincent james 2022 09 29 meta s new text to video ai generator is like dall e for video the verge retrieved 2022 11 09 previous shared tasks conll www conll org retrieved 2021 01 11 cognition lexico oxford university press and dictionary com archived from the original on july 15 2020 retrieved 6 may 2020 ask the cognitive scientist american federation of teachers 8 august 2014 cognitive science is an interdisciplinary field of researchers from linguistics psychology neuroscience philosophy computer science and anthropology that seek to understand the mind robinson peter 2008 handbook of cognitive linguistics and second language acquisition routledge pp 3 8 isbn 978 0 805 85352 0 lakoff george 1999 philosophy in the flesh the embodied mind and its challenge to western philosophy appendix the neural theory of language paradigm new york basic books pp 569 583 isbn 978 0 465 05674 3 strauss claudia 1999 a cognitive theory of cultural meaning cambridge university press pp 156 164 isbn 978 0 521 59541 4 us patent 9269353 universal conceptual cognitive annotation ucca universal conceptual cognitive annotation ucca retrieved 2021 01 11 rodr guez f c mairal us n r 2016 building an rrg computational grammar onomazein 34 86 117 fluid construction grammar a fully operational processing system for construction grammars retrieved 2021 01 11 acl member portal the association for computational linguistics member portal www aclweb org retrieved 2021 01 11 chunks and rules w3c retrieved 2021 01 11 socher richard karpathy andrej le quoc v manning christopher d ng andrew y 2014 grounded compositional semantics for finding and describing images with sentences transactions of the association for computational linguistics 2 207 218 doi 10 1162 tacl a 00177 s2cid 2317858 dasgupta ishita lampinen andrew k chan stephanie c y creswell antonia kumaran dharshan mcclelland james l hill felix 2022 language models show human like content effects on reasoning dasgupta lampinen et al arxiv 2207 07051 cs cl friston karl j 2022 active inference the free energy principle in mind brain and behavior chapter 4 the generative models of active inference the mit press isbn 978 0 262 36997 8 further reading edit bates m 1995 models of natural language understanding proceedings of the national academy of sciences of the united states of america 92 22 9977 9982 bibcode 1995pnas 92 9977b doi 10 1073 pnas 92 22 9977 pmc 40721 pmid 7479812 steven bird ewan klein and edward loper 2009 natural language processing with python o reilly media isbn 978 0 596 51649 9 kenna hughes castleberry a murder mystery puzzle the literary puzzle cain s jawbone which has stumped humans for decades reveals the limitations of natural language processing algorithms scientific american vol 329 no 4 november 2023 pp 81 82 this murder mystery competition has revealed that although nlp natural language processing models are capable of incredible feats their abilities are very much limited by the amount of context they receive this could cause difficulties for researchers who hope to use them to do things such as analyze ancient languages in some cases there are few historical records on long gone civilizations to serve as training data for such a purpose p 82 daniel jurafsky and james h martin 2008 speech and language processing 2nd edition pearson prentice hall isbn 978 0 13 187321 6 mohamed zakaria kurdi 2016 natural language processing and computational linguistics speech morphology and syntax volume 1 iste wiley isbn 978 1848218482 mohamed zakaria kurdi 2017 natural language processing and computational linguistics semantics discourse and applications volume 2 iste wiley isbn 978 1848219212 christopher d manning prabhakar raghavan and hinrich sch tze 2008 introduction to information retrieval cambridge university press isbn 978 0 521 86571 5 official html and pdf versions available without charge christopher d manning and hinrich sch tze 1999 foundations of statistical natural language processing the mit press isbn 978 0 262 13360 9 david m w powers and christopher c r turk 1989 machine learning of natural language springer verlag isbn 978 0 387 19557 5 external links edit media related to natural language processing at wikimedia commons vtenatural language processinggeneral terms ai complete bag of words n gram bigram trigram computational linguistics natural language understanding stop words text processing text analysis argument mining collocation extraction concept mining coreference resolution deep linguistic processing distant reading information extraction named entity recognition ontology learning parsing semantic parsing syntactic parsing part of speech tagging semantic analysis semantic role labeling semantic decomposition semantic similarity sentiment analysis terminology extraction text mining textual entailment truecasing word sense disambiguation word sense induction text segmentation compound term processing lemmatisation lexical analysis text chunking stemming sentence segmentation word segmentation automatic summarization multi document summarization sentence extraction text simplification machine translation computer assisted example based rule based statistical transfer based neural distributional semantics models bert document term matrix explicit semantic analysis fasttext glove language model large latent semantic analysis seq2seq word embedding word2vec language resources datasets and corporatypes andstandards corpus linguistics lexical resource linguistic linked open data machine readable dictionary parallel text propbank semantic network simple knowledge organization system speech corpus text corpus thesaurus information retrieval treebank universal dependencies data babelnet bank of english dbpedia framenet google ngram viewer uby wordnet wikidata automatic identificationand data capture speech recognition speech segmentation speech synthesis natural language generation optical character recognition topic model document classification latent dirichlet allocation pachinko allocation computer assistedreviewing automated essay scoring concordancer grammar checker predictive text pronunciation assessment spell checker natural languageuser interface chatbot interactive fiction question answering virtual assistant voice user interface related formal semantics hallucination natural language toolkit spacy portal language authority control databases nationalunited statesjapanczech republicisraelotheryale lux retrieved from https en wikipedia org w index php title natural language processing oldid 1301380737 categories natural language processingcomputational fields of studycomputational linguisticsspeech recognitionhidden categories all accuracy disputesaccuracy disputes from december 2013harv and sfn no target errorscs1 errors periodical ignoredcs1 maint locationarticles with short descriptionshort description is different from wikidataarticles needing additional references from may 2024all articles needing additional referenceswikipedia articles needing rewrite from july 2025all articles needing rewritewikipedia articles needing reorganization from july 2025articles with multiple maintenance issuesall articles with unsourced statementsarticles with unsourced statements from may 2024commons category link from wikidata this page was last edited on 19 july 2025 at 13 48 utc text is available under the creative commons attribution sharealike 4 0 license additional terms may apply by using this site you agree to the terms of use and privacy policy wikipedia is a registered trademark of the wikimedia foundation inc a non profit organization privacy policy about wikipedia disclaimers contact wikipedia code of conduct developers statistics cookie statement mobile view search search toggle the table of contents natural language processing 71 languages add topic
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tokens_without_numbers: list[str] = [
token for token in tokens if not any(char.isdigit() for char in token)
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tokens_without_stopwords: list[str] = [
token for token in tokens_without_numbers if token not in stopwords.words("english")
]print(tokens_without_stopwords)[ 'natural', 'language', 'processing', 'wikipedia', 'jump', 'content', 'main', 'menu', 'main', 'menu', 'move', 'sidebar', 'hide', 'navigation', 'main', 'pagecontentscurrent', 'eventsrandom', 'articleabout', 'wikipediacontact', 'us', 'contribute', 'helplearn', 'editcommunity', 'portalrecent', 'changesupload', 'filespecial', 'pages', 'search', 'search', 'appearance', 'donate', 'create', 'account', 'log', 'personal', 'tools', 'donate', 'create', 'account', 'log', 'pages', 'logged', 'editors', 'learn', 'contributionstalk', 'contents', 'move', 'sidebar', 'hide', 'top', 'history', 'toggle', 'history', 'subsection', 'symbolic', 'nlp', 'early', 'statistical', 'nlp', 'present', 'approaches', 'symbolic', 'statistical', 'neural', 'networks', 'toggle', 'approaches', 'symbolic', 'statistical', 'neural', 'networks', 'subsection', 'statistical', 'approach', 'neural', 'networks', 'common', 'nlp', 'tasks', 'toggle', 'common', 'nlp', 'tasks', 'subsection', 'text', 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'english', 'srpskisrpskohrvatski', 'suomi', 'rk', 'e', 'ti', 'ng', 'vi', 'edit', 'links', 'articletalk', 'english', 'readeditview', 'history', 'tools', 'tools', 'move', 'sidebar', 'hide', 'actions', 'readeditview', 'history', 'general', 'links', 'hererelated', 'changesupload', 'filepermanent', 'linkpage', 'informationcite', 'pageget', 'shortened', 'urldownload', 'qr', 'code', 'print', 'export', 'download', 'pdfprintable', 'version', 'projects', 'wikimedia', 'commonswikiversitywikidata', 'item', 'appearance', 'move', 'sidebar', 'hide', 'wikipedia', 'free', 'encyclopedia', 'processing', 'natural', 'language', 'computer', 'article', 'multiple', 'issues', 'please', 'help', 'improve', 'discuss', 'issues', 'talk', 'page', 'learn', 'remove', 'messages', 'article', 'needs', 'additional', 'citations', 'verification', 'please', 'help', 'improve', 'article', 'adding', 'citations', 'reliable', 'sources', 'unsourced', 'material', 'may', 'challenged', 'removed', 'find', 'sources', 'natural', 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'recognition', 'lecture', 'notes', 'computer', 'science', 'vol', 'springer', 'berlin', 'heidelberg', 'pp', 'citeseerx', 'doi', 'isbn', 'b', 'natural', 'language', 'processing', 'nlp', 'complete', 'guide', 'www', 'deeplearning', 'ai', 'retrieved', 'natural', 'language', 'processing', 'intro', 'nlp', 'machine', 'learning', 'gyansetu', 'retrieved', 'kishorjit', 'n', 'vidya', 'raj', 'rk', 'nirmal', 'sivaji', 'b', 'manipuri', 'morpheme', 'identification', 'pdf', 'proceedings', 'workshop', 'south', 'southeast', 'asian', 'natural', 'language', 'processing', 'sanlp', 'coling', 'mumbai', 'december', 'cite', 'journal', 'maint', 'location', 'link', 'klein', 'dan', 'manning', 'christopher', 'natural', 'language', 'grammar', 'induction', 'using', 'constituent', 'context', 'model', 'pdf', 'advances', 'neural', 'information', 'processing', 'systems', 'kariampuzha', 'william', 'alyea', 'gioconda', 'qu', 'sue', 'sanjak', 'jaleal', 'math', 'ewy', 'sid', 'eric', 'chatelaine', 'haley', 'yadaw', 'arjun', 'xu', 'yanji', 'zhu', 'qian', 'precision', 'information', 'extraction', 'rare', 'disease', 'epidemiology', 'scale', 'journal', 'translational', 'medicine', 'doi', 'pmc', 'pmid', 'pascal', 'recognizing', 'textual', 'entailment', 'challenge', 'rte', 'https', 'tac', 'nist', 'gov', 'rte', 'lippi', 'marco', 'torroni', 'paolo', 'argumentation', 'mining', 'state', 'art', 'emerging', 'trends', 'acm', 'transactions', 'internet', 'technology', 'doi', 'hdl', 'issn', 'argument', 'mining', 'tutorial', 'www', 'unice', 'fr', 'retrieved', 'nlp', 'approaches', 'computational', 'argumentation', 'acl', 'berlin', 'retrieved', 'administration', 'centre', 'language', 'technology', 'clt', 'macquarie', 'university', 'retrieved', 'shared', 'task', 'grammatical', 'error', 'correction', 'www', 'comp', 'nus', 'edu', 'sg', 'retrieved', 'shared', 'task', 'grammatical', 'error', 'correction', 'www', 'comp', 'nus', 'edu', 'sg', 'retrieved', 'duan', 'yucong', 'cruz', 'christophe', 'formalizing', 'semantic', 'natural', 'language', 'conceptualization', 'existence', 'international', 'journal', 'innovation', 'management', 'technology', 'archived', 'original', 'u', 'b', 'u', 'w', 'e', 'b', 'racter', 'www', 'ubu', 'com', 'retrieved', 'writer', 'beta', 'lithium', 'ion', 'batteries', 'doi', 'isbn', 'document', 'understanding', 'ai', 'google', 'cloud', 'cloud', 'next', 'youtube', 'www', 'youtube', 'com', 'april', 'archived', 'original', 'retrieved', 'robertson', 'adi', 'openai', 'dall', 'e', 'ai', 'image', 'generator', 'edit', 'pictures', 'verge', 'retrieved', 'stanford', 'natural', 'language', 'processing', 'group', 'nlp', 'stanford', 'edu', 'retrieved', 'coyne', 'bob', 'sproat', 'richard', 'wordseye', 'proceedings', 'annual', 'conference', 'computer', 'graphics', 'interactive', 'techniques', 'siggraph', 'new', 'york', 'ny', 'usa', 'association', 'computing', 'machinery', 'pp', 'doi', 'isbn', 'google', 'announces', 'ai', 'advances', 'text', 'video', 'language', 'translation', 'venturebeat', 'retrieved', 'vincent', 'james', 'meta', 'new', 'text', 'video', 'ai', 'generator', 'like', 'dall', 'e', 'video', 'verge', 'retrieved', 'previous', 'shared', 'tasks', 'conll', 'www', 'conll', 'org', 'retrieved', 'cognition', 'lexico', 'oxford', 'university', 'press', 'dictionary', 'com', 'archived', 'original', 'july', 'retrieved', 'may', 'ask', 'cognitive', 'scientist', 'american', 'federation', 'teachers', 'august', 'cognitive', 'science', 'interdisciplinary', 'field', 'researchers', 'linguistics', 'psychology', 'neuroscience', 'philosophy', 'computer', 'science', 'anthropology', 'seek', 'understand', 'mind', 'robinson', 'peter', 'handbook', 'cognitive', 'linguistics', 'second', 'language', 'acquisition', 'routledge', 'pp', 'isbn', 'lakoff', 'george', 'philosophy', 'flesh', 'embodied', 'mind', 'challenge', 'western', 'philosophy', 'appendix', 'neural', 'theory', 'language', 'paradigm', 'new', 'york', 'basic', 'books', 'pp', 'isbn', 'strauss', 'claudia', 'cognitive', 'theory', 'cultural', 'meaning', 'cambridge', 'university', 'press', 'pp', 'isbn', 'us', 'patent', 'universal', 'conceptual', 'cognitive', 'annotation', 'ucca', 'universal', 'conceptual', 'cognitive', 'annotation', 'ucca', 'retrieved', 'rodr', 'guez', 'f', 'c', 'mairal', 'us', 'n', 'r', 'building', 'rrg', 'computational', 'grammar', 'onomazein', 'fluid', 'construction', 'grammar', 'fully', 'operational', 'processing', 'system', 'construction', 'grammars', 'retrieved', 'acl', 'member', 'portal', 'association', 'computational', 'linguistics', 'member', 'portal', 'www', 'aclweb', 'org', 'retrieved', 'chunks', 'rules', 'retrieved', 'socher', 'richard', 'karpathy', 'andrej', 'le', 'quoc', 'v', 'manning', 'christopher', 'ng', 'andrew', 'grounded', 'compositional', 'semantics', 'finding', 'describing', 'images', 'sentences', 'transactions', 'association', 'computational', 'linguistics', 'doi', 'tacl', 'dasgupta', 'ishita', 'lampinen', 'andrew', 'k', 'chan', 'stephanie', 'c', 'creswell', 'antonia', 'kumaran', 'dharshan', 'mcclelland', 'james', 'l', 'hill', 'felix', 'language', 'models', 'show', 'human', 'like', 'content', 'effects', 'reasoning', 'dasgupta', 'lampinen', 'et', 'al', 'arxiv', 'cs', 'cl', 'friston', 'karl', 'j', 'active', 'inference', 'free', 'energy', 'principle', 'mind', 'brain', 'behavior', 'chapter', 'generative', 'models', 'active', 'inference', 'mit', 'press', 'isbn', 'reading', 'edit', 'bates', 'models', 'natural', 'language', 'understanding', 'proceedings', 'national', 'academy', 'sciences', 'united', 'states', 'america', 'bibcode', 'doi', 'pnas', 'pmc', 'pmid', 'steven', 'bird', 'ewan', 'klein', 'edward', 'loper', 'natural', 'language', 'processing', 'python', 'reilly', 'media', 'isbn', 'kenna', 'hughes', 'castleberry', 'murder', 'mystery', 'puzzle', 'literary', 'puzzle', 'cain', 'jawbone', 'stumped', 'humans', 'decades', 'reveals', 'limitations', 'natural', 'language', 'processing', 'algorithms', 'scientific', 'american', 'vol', 'november', 'pp', 'murder', 'mystery', 'competition', 'revealed', 'although', 'nlp', 'natural', 'language', 'processing', 'models', 'capable', 'incredible', 'feats', 'abilities', 'much', 'limited', 'amount', 'context', 'receive', 'could', 'cause', 'difficulties', 'researchers', 'hope', 'use', 'things', 'analyze', 'ancient', 'languages', 'cases', 'historical', 'records', 'long', 'gone', 'civilizations', 'serve', 'training', 'data', 'purpose', 'p', 'daniel', 'jurafsky', 'james', 'h', 'martin', 'speech', 'language', 'processing', 'edition', 'pearson', 'prentice', 'hall', 'isbn', 'mohamed', 'zakaria', 'kurdi', 'natural', 'language', 'processing', 'computational', 'linguistics', 'speech', 'morphology', 'syntax', 'volume', 'iste', 'wiley', 'isbn', 'mohamed', 'zakaria', 'kurdi', 'natural', 'language', 'processing', 'computational', 'linguistics', 'semantics', 'discourse', 'applications', 'volume', 'iste', 'wiley', 'isbn', 'christopher', 'manning', 'prabhakar', 'raghavan', 'hinrich', 'sch', 'tze', 'introduction', 'information', 'retrieval', 'cambridge', 'university', 'press', 'isbn', 'official', 'html', 'pdf', 'versions', 'available', 'without', 'charge', 'christopher', 'manning', 'hinrich', 'sch', 'tze', 'foundations', 'statistical', 'natural', 'language', 'processing', 'mit', 'press', 'isbn', 'david', 'w', 'powers', 'christopher', 'c', 'r', 'turk', 'machine', 'learning', 'natural', 'language', 'springer', 'verlag', 'isbn', 'external', 'links', 'edit', 'media', 'related', 'natural', 'language', 'processing', 'wikimedia', 'commons', 'vtenatural', 'language', 'processinggeneral', 'terms', 'ai', 'complete', 'bag', 'words', 'n', 'gram', 'bigram', 'trigram', 'computational', 'linguistics', 'natural', 'language', 'understanding', 'stop', 'words', 'text', 'processing', 'text', 'analysis', 'argument', 'mining', 'collocation', 'extraction', 'concept', 'mining', 'coreference', 'resolution', 'deep', 'linguistic', 'processing', 'distant', 'reading', 'information', 'extraction', 'named', 'entity', 'recognition', 'ontology', 'learning', 'parsing', 'semantic', 'parsing', 'syntactic', 'parsing', 'part', 'speech', 'tagging', 'semantic', 'analysis', 'semantic', 'role', 'labeling', 'semantic', 'decomposition', 'semantic', 'similarity', 'sentiment', 'analysis', 'terminology', 'extraction', 'text', 'mining', 'textual', 'entailment', 'truecasing', 'word', 'sense', 'disambiguation', 'word', 'sense', 'induction', 'text', 'segmentation', 'compound', 'term', 'processing', 'lemmatisation', 'lexical', 'analysis', 'text', 'chunking', 'stemming', 'sentence', 'segmentation', 'word', 'segmentation', 'automatic', 'summarization', 'multi', 'document', 'summarization', 'sentence', 'extraction', 'text', 'simplification', 'machine', 'translation', 'computer', 'assisted', 'example', 'based', 'rule', 'based', 'statistical', 'transfer', 'based', 'neural', 'distributional', 'semantics', 'models', 'bert', 'document', 'term', 'matrix', 'explicit', 'semantic', 'analysis', 'fasttext', 'glove', 'language', 'model', 'large', 'latent', 'semantic', 'analysis', 'word', 'embedding', 'language', 'resources', 'datasets', 'corporatypes', 'andstandards', 'corpus', 'linguistics', 'lexical', 'resource', 'linguistic', 'linked', 'open', 'data', 'machine', 'readable', 'dictionary', 'parallel', 'text', 'propbank', 'semantic', 'network', 'simple', 'knowledge', 'organization', 'system', 'speech', 'corpus', 'text', 'corpus', 'thesaurus', 'information', 'retrieval', 'treebank', 'universal', 'dependencies', 'data', 'babelnet', 'bank', 'english', 'dbpedia', 'framenet', 'google', 'ngram', 'viewer', 'uby', 'wordnet', 'wikidata', 'automatic', 'identificationand', 'data', 'capture', 'speech', 'recognition', 'speech', 'segmentation', 'speech', 'synthesis', 'natural', 'language', 'generation', 'optical', 'character', 'recognition', 'topic', 'model', 'document', 'classification', 'latent', 'dirichlet', 'allocation', 'pachinko', 'allocation', 'computer', 'assistedreviewing', 'automated', 'essay', 'scoring', 'concordancer', 'grammar', 'checker', 'predictive', 'text', 'pronunciation', 'assessment', 'spell', 'checker', 'natural', 'languageuser', 'interface', 'chatbot', 'interactive', 'fiction', 'question', 'answering', 'virtual', 'assistant', 'voice', 'user', 'interface', 'related', 'formal', 'semantics', 'hallucination', 'natural', 'language', 'toolkit', 'spacy', 'portal', 'language', 'authority', 'control', 'databases', 'nationalunited', 'statesjapanczech', 'republicisraelotheryale', 'lux', 'retrieved', 'https', 'en', 'wikipedia', 'org', 'w', 'index', 'php', 'title', 'natural', 'language', 'processing', 'oldid', 'categories', 'natural', 'language', 'processingcomputational', 'fields', 'studycomputational', 'linguisticsspeech', 'recognitionhidden', 'categories', 'accuracy', 'disputesaccuracy', 'disputes', 'december', 'sfn', 'target', 'errors', 'periodical', 'maint', 'locationarticles', 'short', 'descriptionshort', 'description', 'different', 'wikidataarticles', 'needing', 'additional', 'references', 'may', 'articles', 'needing', 'additional', 'referenceswikipedia', 'articles', 'needing', 'rewrite', 'july', 'articles', 'needing', 'rewritewikipedia', 'articles', 'needing', 'reorganization', 'july', 'multiple', 'maintenance', 'issuesall', 'articles', 'unsourced', 'statementsarticles', 'unsourced', 'statements', 'may', 'category', 'link', 'wikidata', 'page', 'last', 'edited', 'july', 'utc', 'text', 'available', 'creative', 'commons', 'attribution', 'sharealike', 'license', 'additional', 'terms', 'may', 'apply', 'using', 'site', 'agree', 'terms', 'use', 'privacy', 'policy', 'wikipedia', 'registered', 'trademark', 'wikimedia', 'foundation', 'inc', 'non', 'profit', 'organization', 'privacy', 'policy', 'wikipedia', 'disclaimers', 'contact', 'wikipedia', 'code', 'conduct', 'developers', 'statistics', 'cookie', 'statement', 'mobile', 'view', 'search', 'search', 'toggle', 'table', 'contents', 'natural', 'language', 'processing', 'languages', 'add', 'topic' ]
token_less_3: list[str] = [
token for token in tokens_without_stopwords if len(token) < 3
]print(sorted(token_less_3))[ 'ai', 'ai', 'ai', 'ai', 'ai', 'ai', 'ai', 'ai', 'ai', 'ai', 'ai', 'ai', 'ai', 'ai', 'al', 'al', 'al', 'az', 'b', 'b', 'b', 'b', 'b', 'c', 'c', 'c', 'c', 'cl', 'co', 'cs', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'e', 'en', 'et', 'et', 'et', 'f', 'f', 'f', 'fr', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'h', 'j', 'j', 'j', 'k', 'k', 'k', 'k', 'k', 'k', 'k', 'k', 'l', 'l', 'l', 'le', 'mt', 'mt', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'ng', 'ng', 'ny', 'p', 'p', 'p', 'p', 'p', 'p', 'p', 'pf', 'pf', 'pp', 'pp', 'pp', 'pp', 'pp', 'pp', 'pp', 'pp', 'pp', 'pp', 'qr', 'qu', 'r', 'r', 'r', 'r', 'r', 'rk', 'rk', 'sg', 'sg', 'tf', 'ti', 'u', 'u', 'us', 'us', 'us', 'us', 'v', 'vi', 'vs', 'vs', 'w', 'w', 'w', 'wu', 'xu', 'yi' ]
token_greater_2: list[str] = [
token for token in tokens_without_stopwords if len(token) > 2
]print(token_greater_2)[ 'natural', 'language', 'processing', 'wikipedia', 'jump', 'content', 'main', 'menu', 'main', 'menu', 'move', 'sidebar', 'hide', 'navigation', 'main', 'pagecontentscurrent', 'eventsrandom', 'articleabout', 'wikipediacontact', 'contribute', 'helplearn', 'editcommunity', 'portalrecent', 'changesupload', 'filespecial', 'pages', 'search', 'search', 'appearance', 'donate', 'create', 'account', 'log', 'personal', 'tools', 'donate', 'create', 'account', 'log', 'pages', 'logged', 'editors', 'learn', 'contributionstalk', 'contents', 'move', 'sidebar', 'hide', 'top', 'history', 'toggle', 'history', 'subsection', 'symbolic', 'nlp', 'early', 'statistical', 'nlp', 'present', 'approaches', 'symbolic', 'statistical', 'neural', 'networks', 'toggle', 'approaches', 'symbolic', 'statistical', 'neural', 'networks', 'subsection', 'statistical', 'approach', 'neural', 'networks', 'common', 'nlp', 'tasks', 'toggle', 'common', 'nlp', 'tasks', 'subsection', 'text', 'speech', 'processing', 'morphological', 'analysis', 'syntactic', 'analysis', 'lexical', 'semantics', 'individual', 'words', 'context', 'relational', 'semantics', 'semantics', 'individual', 'sentences', 'discourse', 'semantics', 'beyond', 'individual', 'sentences', 'higher', 'level', 'nlp', 'applications', 'general', 'tendencies', 'possible', 'future', 'directions', 'toggle', 'general', 'tendencies', 'possible', 'future', 'directions', 'subsection', 'cognition', 'see', 'also', 'references', 'reading', 'external', 'links', 'toggle', 'table', 'contents', 'natural', 'language', 'processing', 'languages', 'afrikaans', 'rbaycanca', 'bosanskibrezhonegcatal', 'tinacymraegdanskdeutscheesti', 'espa', 'olesperantoeuskara', 'fran', 'aisgaeilgegalego', 'hrvatskiidobahasa', 'indonesiaisizulu', 'slenskaitaliano', 'latvie', 'ulietuvi', 'nederlands', 'norsk', 'bokm', 'picardpiemont', 'ispolskiportugu', 'sqaraqalpaqsharom', 'runa', 'simi', 'shqipsimple', 'english', 'srpskisrpskohrvatski', 'suomi', 'edit', 'links', 'articletalk', 'english', 'readeditview', 'history', 'tools', 'tools', 'move', 'sidebar', 'hide', 'actions', 'readeditview', 'history', 'general', 'links', 'hererelated', 'changesupload', 'filepermanent', 'linkpage', 'informationcite', 'pageget', 'shortened', 'urldownload', 'code', 'print', 'export', 'download', 'pdfprintable', 'version', 'projects', 'wikimedia', 'commonswikiversitywikidata', 'item', 'appearance', 'move', 'sidebar', 'hide', 'wikipedia', 'free', 'encyclopedia', 'processing', 'natural', 'language', 'computer', 'article', 'multiple', 'issues', 'please', 'help', 'improve', 'discuss', 'issues', 'talk', 'page', 'learn', 'remove', 'messages', 'article', 'needs', 'additional', 'citations', 'verification', 'please', 'help', 'improve', 'article', 'adding', 'citations', 'reliable', 'sources', 'unsourced', 'material', 'may', 'challenged', 'removed', 'find', 'sources', 'natural', 'language', 'processing', 'news', 'newspapers', 'books', 'scholar', 'jstor', 'may', 'learn', 'remove', 'message', 'article', 'may', 'need', 'rewritten', 'comply', 'wikipedia', 'quality', 'standards', 'help', 'talk', 'page', 'may', 'contain', 'suggestions', 'july', 'article', 'may', 'need', 'reorganization', 'comply', 'wikipedia', 'layout', 'guidelines', 'please', 'help', 'editing', 'article', 'make', 'improvements', 'overall', 'structure', 'july', 'learn', 'remove', 'message', 'learn', 'remove', 'message', 'natural', 'language', 'processing', 'nlp', 'processing', 'natural', 'language', 'information', 'computer', 'study', 'nlp', 'subfield', 'computer', 'science', 'generally', 'associated', 'artificial', 'intelligence', 'nlp', 'related', 'information', 'retrieval', 'knowledge', 'representation', 'computational', 'linguistics', 'broadly', 'linguistics', 'major', 'processing', 'tasks', 'nlp', 'system', 'include', 'speech', 'recognition', 'text', 'classification', 'natural', 'language', 'understanding', 'natural', 'language', 'generation', 'history', 'edit', 'information', 'history', 'natural', 'language', 'processing', 'natural', 'language', 'processing', 'roots', 'already', 'alan', 'turing', 'published', 'article', 'titled', 'computing', 'machinery', 'intelligence', 'proposed', 'called', 'turing', 'test', 'criterion', 'intelligence', 'though', 'time', 'articulated', 'problem', 'separate', 'artificial', 'intelligence', 'proposed', 'test', 'includes', 'task', 'involves', 'automated', 'interpretation', 'generation', 'natural', 'language', 'symbolic', 'nlp', 'early', 'edit', 'premise', 'symbolic', 'nlp', 'well', 'summarized', 'john', 'searle', 'chinese', 'room', 'experiment', 'given', 'collection', 'rules', 'chinese', 'phrasebook', 'questions', 'matching', 'answers', 'computer', 'emulates', 'natural', 'language', 'understanding', 'nlp', 'tasks', 'applying', 'rules', 'data', 'confronts', 'georgetown', 'experiment', 'involved', 'fully', 'automatic', 'translation', 'sixty', 'russian', 'sentences', 'english', 'authors', 'claimed', 'within', 'three', 'five', 'years', 'machine', 'translation', 'would', 'solved', 'problem', 'however', 'real', 'progress', 'much', 'slower', 'alpac', 'report', 'found', 'ten', 'years', 'research', 'failed', 'fulfill', 'expectations', 'funding', 'machine', 'translation', 'dramatically', 'reduced', 'little', 'research', 'machine', 'translation', 'conducted', 'america', 'though', 'research', 'continued', 'elsewhere', 'japan', 'europe', 'late', 'first', 'statistical', 'machine', 'translation', 'systems', 'developed', 'notably', 'successful', 'natural', 'language', 'processing', 'systems', 'developed', 'shrdlu', 'natural', 'language', 'system', 'working', 'restricted', 'blocks', 'worlds', 'restricted', 'vocabularies', 'eliza', 'simulation', 'rogerian', 'psychotherapist', 'written', 'joseph', 'weizenbaum', 'using', 'almost', 'information', 'human', 'thought', 'emotion', 'eliza', 'sometimes', 'provided', 'startlingly', 'human', 'like', 'interaction', 'patient', 'exceeded', 'small', 'knowledge', 'base', 'eliza', 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'articles', 'unsourced', 'statementsarticles', 'unsourced', 'statements', 'may', 'category', 'link', 'wikidata', 'page', 'last', 'edited', 'july', 'utc', 'text', 'available', 'creative', 'commons', 'attribution', 'sharealike', 'license', 'additional', 'terms', 'may', 'apply', 'using', 'site', 'agree', 'terms', 'use', 'privacy', 'policy', 'wikipedia', 'registered', 'trademark', 'wikimedia', 'foundation', 'inc', 'non', 'profit', 'organization', 'privacy', 'policy', 'wikipedia', 'disclaimers', 'contact', 'wikipedia', 'code', 'conduct', 'developers', 'statistics', 'cookie', 'statement', 'mobile', 'view', 'search', 'search', 'toggle', 'table', 'contents', 'natural', 'language', 'processing', 'languages', 'add', 'topic' ]
ps = PorterStemmer()
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'analysi', 'word', 'embed', 'languag', 'resourc', 'dataset', 'corporatyp', 'andstandard', 'corpu', 'linguist', 'lexic', 'resourc', 'linguist', 'link', 'open', 'data', 'machin', 'readabl', 'dictionari', 'parallel', 'text', 'propbank', 'semant', 'network', 'simpl', 'knowledg', 'organ', 'system', 'speech', 'corpu', 'text', 'corpu', 'thesauru', 'inform', 'retriev', 'treebank', 'univers', 'depend', 'data', 'babelnet', 'bank', 'english', 'dbpedia', 'framenet', 'googl', 'ngram', 'viewer', 'ubi', 'wordnet', 'wikidata', 'automat', 'identificationand', 'data', 'captur', 'speech', 'recognit', 'speech', 'segment', 'speech', 'synthesi', 'natur', 'languag', 'gener', 'optic', 'charact', 'recognit', 'topic', 'model', 'document', 'classif', 'latent', 'dirichlet', 'alloc', 'pachinko', 'alloc', 'comput', 'assistedreview', 'autom', 'essay', 'score', 'concordanc', 'grammar', 'checker', 'predict', 'text', 'pronunci', 'assess', 'spell', 'checker', 'natur', 'languageus', 'interfac', 'chatbot', 'interact', 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'may', 'categori', 'link', 'wikidata', 'page', 'last', 'edit', 'juli', 'utc', 'text', 'avail', 'creativ', 'common', 'attribut', 'sharealik', 'licens', 'addit', 'term', 'may', 'appli', 'use', 'site', 'agre', 'term', 'use', 'privaci', 'polici', 'wikipedia', 'regist', 'trademark', 'wikimedia', 'foundat', 'inc', 'non', 'profit', 'organ', 'privaci', 'polici', 'wikipedia', 'disclaim', 'contact', 'wikipedia', 'code', 'conduct', 'develop', 'statist', 'cooki', 'statement', 'mobil', 'view', 'search', 'search', 'toggl', 'tabl', 'content', 'natur', 'languag', 'process', 'languag', 'add', 'topic' ]
lemmatizer = WordNetLemmatizer()
token_lemmatizer: list[str] = [lemmatizer.lemmatize(token) for token in token_stemming]print(token_lemmatizer)[ 'natur', 'languag', 'process', 'wikipedia', 'jump', 'content', 'main', 'menu', 'main', 'menu', 'move', 'sidebar', 'hide', 'navig', 'main', 'pagecontentscurr', 'eventsrandom', 'articleabout', 'wikipediacontact', 'contribut', 'helplearn', 'editcommun', 'portalrec', 'changesupload', 'filespeci', 'page', 'search', 'search', 'appear', 'donat', 'creat', 'account', 'log', 'person', 'tool', 'donat', 'creat', 'account', 'log', 'page', 'log', 'editor', 'learn', 'contributionstalk', 'content', 'move', 'sidebar', 'hide', 'top', 'histori', 'toggl', 'histori', 'subsect', 'symbol', 'nlp', 'earli', 'statist', 'nlp', 'present', 'approach', 'symbol', 'statist', 'neural', 'network', 'toggl', 'approach', 'symbol', 'statist', 'neural', 'network', 'subsect', 'statist', 'approach', 'neural', 'network', 'common', 'nlp', 'task', 'toggl', 'common', 'nlp', 'task', 'subsect', 'text', 'speech', 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'pmid', 'steven', 'bird', 'ewan', 'klein', 'edward', 'loper', 'natur', 'languag', 'process', 'python', 'reilli', 'medium', 'isbn', 'kenna', 'hugh', 'castleberri', 'murder', 'mysteri', 'puzzl', 'literari', 'puzzl', 'cain', 'jawbon', 'stump', 'human', 'decad', 'reveal', 'limit', 'natur', 'languag', 'process', 'algorithm', 'scientif', 'american', 'vol', 'novemb', 'murder', 'mysteri', 'competit', 'reveal', 'although', 'nlp', 'natur', 'languag', 'process', 'model', 'capabl', 'incred', 'feat', 'abil', 'much', 'limit', 'amount', 'context', 'receiv', 'could', 'caus', 'difficulti', 'research', 'hope', 'use', 'thing', 'analyz', 'ancient', 'languag', 'case', 'histor', 'record', 'long', 'gone', 'civil', 'serv', 'train', 'data', 'purpos', 'daniel', 'jurafski', 'jame', 'martin', 'speech', 'languag', 'process', 'edit', 'pearson', 'prentic', 'hall', 'isbn', 'moham', 'zakaria', 'kurdi', 'natur', 'languag', 'process', 'comput', 'linguist', 'speech', 'morpholog', 'syntax', 'volum', 'ist', 'wiley', 'isbn', 'moham', 'zakaria', 'kurdi', 'natur', 'languag', 'process', 'comput', 'linguist', 'semant', 'discours', 'applic', 'volum', 'ist', 'wiley', 'isbn', 'christoph', 'man', 'prabhakar', 'raghavan', 'hinrich', 'sch', 'tze', 'introduct', 'inform', 'retriev', 'cambridg', 'univers', 'press', 'isbn', 'offici', 'html', 'pdf', 'version', 'avail', 'without', 'charg', 'christoph', 'man', 'hinrich', 'sch', 'tze', 'foundat', 'statist', 'natur', 'languag', 'process', 'mit', 'press', 'isbn', 'david', 'power', 'christoph', 'turk', 'machin', 'learn', 'natur', 'languag', 'springer', 'verlag', 'isbn', 'extern', 'link', 'edit', 'medium', 'relat', 'natur', 'languag', 'process', 'wikimedia', 'common', 'vtenatur', 'languag', 'processinggener', 'term', 'complet', 'bag', 'word', 'gram', 'bigram', 'trigram', 'comput', 'linguist', 'natur', 'languag', 'understand', 'stop', 'word', 'text', 'process', 'text', 'analysi', 'argument', 'mine', 'colloc', 'extract', 'concept', 'mine', 'corefer', 'resolut', 'deep', 'linguist', 'process', 'distant', 'read', 'inform', 'extract', 'name', 'entiti', 'recognit', 'ontolog', 'learn', 'par', 'semant', 'par', 'syntact', 'par', 'part', 'speech', 'tag', 'semant', 'analysi', 'semant', 'role', 'label', 'semant', 'decomposit', 'semant', 'similar', 'sentiment', 'analysi', 'terminolog', 'extract', 'text', 'mine', 'textual', 'entail', 'truecas', 'word', 'sen', 'disambigu', 'word', 'sen', 'induct', 'text', 'segment', 'compound', 'term', 'process', 'lemmatis', 'lexic', 'analysi', 'text', 'chunk', 'stem', 'sentenc', 'segment', 'word', 'segment', 'automat', 'summar', 'multi', 'document', 'summar', 'sentenc', 'extract', 'text', 'simplif', 'machin', 'translat', 'comput', 'assist', 'exampl', 'base', 'rule', 'base', 'statist', 'transfer', 'base', 'neural', 'distribut', 'semant', 'model', 'bert', 'document', 'term', 'matrix', 'explicit', 'semant', 'analysi', 'fasttext', 'glove', 'languag', 'model', 'larg', 'latent', 'semant', 'analysi', 'word', 'embed', 'languag', 'resourc', 'dataset', 'corporatyp', 'andstandard', 'corpu', 'linguist', 'lexic', 'resourc', 'linguist', 'link', 'open', 'data', 'machin', 'readabl', 'dictionari', 'parallel', 'text', 'propbank', 'semant', 'network', 'simpl', 'knowledg', 'organ', 'system', 'speech', 'corpu', 'text', 'corpu', 'thesauru', 'inform', 'retriev', 'treebank', 'univers', 'depend', 'data', 'babelnet', 'bank', 'english', 'dbpedia', 'framenet', 'googl', 'ngram', 'viewer', 'ubi', 'wordnet', 'wikidata', 'automat', 'identificationand', 'data', 'captur', 'speech', 'recognit', 'speech', 'segment', 'speech', 'synthesi', 'natur', 'languag', 'gener', 'optic', 'charact', 'recognit', 'topic', 'model', 'document', 'classif', 'latent', 'dirichlet', 'alloc', 'pachinko', 'alloc', 'comput', 'assistedreview', 'autom', 'essay', 'score', 'concordanc', 'grammar', 'checker', 'predict', 'text', 'pronunci', 'assess', 'spell', 'checker', 'natur', 'languageus', 'interfac', 'chatbot', 'interact', 'fiction', 'question', 'answer', 'virtual', 'assist', 'voic', 'user', 'interfac', 'relat', 'formal', 'semant', 'hallucin', 'natur', 'languag', 'toolkit', 'spaci', 'portal', 'languag', 'author', 'control', 'databas', 'nationalunit', 'statesjapanczech', 'republicisraelotheryal', 'lux', 'retriev', 'http', 'wikipedia', 'org', 'index', 'php', 'titl', 'natur', 'languag', 'process', 'oldid', 'categori', 'natur', 'languag', 'processingcomput', 'field', 'studycomput', 'linguisticsspeech', 'recognitionhidden', 'categori', 'accuraci', 'disputesaccuraci', 'disput', 'decemb', 'sfn', 'target', 'error', 'period', 'maint', 'locationarticl', 'short', 'descriptionshort', 'descript', 'differ', 'wikidataarticl', 'need', 'addit', 'refer', 'may', 'articl', 'need', 'addit', 'referenceswikipedia', 'articl', 'need', 'rewrit', 'juli', 'articl', 'need', 'rewritewikipedia', 'articl', 'need', 'reorgan', 'juli', 'multipl', 'mainten', 'issuesal', 'articl', 'unsourc', 'statementsarticl', 'unsourc', 'statement', 'may', 'categori', 'link', 'wikidata', 'page', 'last', 'edit', 'juli', 'utc', 'text', 'avail', 'creativ', 'common', 'attribut', 'sharealik', 'licens', 'addit', 'term', 'may', 'appli', 'use', 'site', 'agre', 'term', 'use', 'privaci', 'polici', 'wikipedia', 'regist', 'trademark', 'wikimedia', 'foundat', 'inc', 'non', 'profit', 'organ', 'privaci', 'polici', 'wikipedia', 'disclaim', 'contact', 'wikipedia', 'code', 'conduct', 'develop', 'statist', 'cooki', 'statement', 'mobil', 'view', 'search', 'search', 'toggl', 'tabl', 'content', 'natur', 'languag', 'process', 'languag', 'add', 'topic' ]
unique_words: list[str] = list(set(token_lemmatizer))print(sorted(unique_words))[ 'aaron', 'abbrevi', 'abelson', 'abil', 'abl', 'abstract', 'academi', 'accent', 'access', 'accident', 'accord', 'account', 'accur', 'accuraci', 'achiev', 'acl', 'aclweb', 'acm', 'acquir', 'acquisit', 'act', 'action', 'activ', 'actual', 'ad', 'add', 'addit', 'address', 'adi', 'adject', 'administr', 'advanc', 'advantag', 'affect', 'afrikaan', 'age', 'agent', 'agglutin', 'agre', 'aid', 'aisgaeilgegalego', 'alan', 'alexand', 'algorithm', 'align', 'alik', 'alloc', 'allow', 'almost', 'along', 'alpac', 'alphabet', 'alreadi', 'also', 'although', 'alyea', 'ambigu', 'america', 'american', 'among', 'amount', 'amr', 'analog', 'analys', 'analysi', 'analyst', 'analyz', 'anaphora', 'ancient', 'andrej', 'andrew', 'andstandard', 'annot', 'announc', 'annual', 'anoth', 'answer', 'anthropolog', 'antonia', 'anymor', 'apertium', 'appar', 'appear', 'appendix', 'appli', 'applic', 'approach', 'april', 'arab', 'archiv', 'area', 'argument', 'arjun', 'art', 'articl', 'articleabout', 'articletalk', 'articul', 'artifici', 'arxiv', 'asian', 'ask', 'aspect', 'assert', 'assess', 'assign', 'assist', 'assistedreview', 'associ', 'assumpt', 'atom', 'attribut', 'august', 'author', 'autom', 'automat', 'automaton', 'avail', 'babelnet', 'background', 'bag', 'band', 'bank', 'base', 'basi', 'basic', 'basqu', 'bate', 'batteri', 'beard', 'becam', 'becom', 'began', 'behavior', 'behaviour', 'behind', 'bengio', 'berlin', 'bert', 'best', 'beta', 'beyond', 'bibcod', 'big', 'bigram', 'biomed', 'bird', 'blend', 'blind', 'block', 'bob', 'bokm', 'book', 'bosanskibrezhonegcat', 'boundari', 'bow', 'brain', 'branch', 'break', 'bridg', 'british', 'brno', 'broadli', 'broken', 'buchanan', 'buil', 'build', 'bulgarian', 'burget', 'busi', 'cain', 'calculu', 'call', 'cambridg', 'camera', 'canada', 'capabl', 'capit', 'captur', 'carbonel', 'care', 'case', 'castleberri', 'catalan', 'categor', 'categori', 'caus', 'center', 'centr', 'certain', 'challeng', 'cham', 'chan', 'chang', 'changesupload', 'chapter', 'charact', 'charg', 'charniak', 'chatbot', 'chatelain', 'chatterbot', 'checker', 'chine', 'choe', 'chomskyan', 'christian', 'christoph', 'chucai', 'chunk', 'citat', 'cite', 'citeseerx', 'civil', 'claim', 'class', 'classif', 'classifi', 'claudia', 'clinic', 'clip', 'close', 'closer', 'cloud', 'clt', 'coars', 'coarticul', 'code', 'cognit', 'cole', 'collect', 'colleg', 'colloc', 'colloqui', 'com', 'combin', 'commerci', 'common', 'commonli', 'commonswikiversitywikidata', 'commun', 'comp', 'compani', 'compar', 'comparison', 'competit', 'complet', 'complex', 'compli', 'compon', 'composit', 'compound', 'comprehens', 'comput', 'concept', 'conceptu', 'concern', 'conclus', 'concordanc', 'conduct', 'confer', 'confront', 'confus', 'conll', 'connect', 'consid', 'consist', 'constitu', 'construct', 'contact', 'contain', 'content', 'context', 'continu', 'contrast', 'contribut', 'contributionstalk', 'control', 'conveni', 'convers', 'convert', 'cooki', 'corefer', 'corner', 'corporatyp', 'corpu', 'corpus', 'correct', 'correspond', 'costli', 'could', 'counter', 'coupl', 'courvil', 'coyn', 'creat', 'creation', 'creativ', 'creswel', 'crevier', 'criterion', 'cruz', 'cullingford', 'cultur', 'current', 'custom', 'cwa', 'cynthia', 'czech', 'dall', 'dan', 'daniel', 'danish', 'dasgupta', 'data', 'databas', 'dataset', 'david', 'day', 'dbpedia', 'deal', 'decad', 'decemb', 'decis', 'decomposit', 'deep', 'deeplearn', 'defin', 'demonstr', 'depart', 'depend', 'deriv', 'describ', 'descript', 'descriptionshort', 'design', 'desir', 'detect', 'determin', 'develop', 'development', 'devi', 'devot', 'dharshan', 'diabet', 'dialogu', 'dictionari', 'differ', 'difficult', 'difficulti', 'digit', 'direct', 'dirichlet', 'disambigu', 'disclaim', 'discourag', 'discours', 'discret', 'discus', 'diseas', 'displaystyl', 'disput', 'disputesaccuraci', 'distant', 'distinguish', 'distribut', 'diver', 'divid', 'divis', 'document', 'doi', 'domin', 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