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class="banner-container"> <div id="siteNotice"></div> </div> <div class="pre-content heading-holder"> <div class="page-heading"> <h1 id="firstHeading" class="firstHeading mw-first-heading">Natural language processing</h1> <div class="tagline"></div> </div> <ul id="p-associated-pages" class="minerva__tab-container"> <li class="minerva__tab selected"> <a class="minerva__tab-text" href="/wiki/Natural_language_processing" rel="" data-event-name="tabs.subject">Article</a> </li> <li class="minerva__tab "> <a class="minerva__tab-text" href="/wiki/Talk:Natural_language_processing" rel="discussion" data-event-name="tabs.talk">Talk</a> </li> </ul> <nav class="page-actions-menu"> <ul id="p-views" class="page-actions-menu__list"> <li id="language-selector" class="page-actions-menu__list-item"> <a role="button" href="#p-lang" data-mw="interface" data-event-name="menu.languages" title="Language" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled 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<div id="mw-content-text" class="mw-body-content"><script>function mfTempOpenSection(id){var block=document.getElementById("mf-section-"+id);block.className+=" open-block";block.previousSibling.className+=" open-block";}</script><div class="mw-content-ltr mw-parser-output" lang="en" dir="ltr"><section class="mf-section-0" id="mf-section-0"> <style data-mw-deduplicate="TemplateStyles:r1236090951">.mw-parser-output .hatnote{font-style:italic}.mw-parser-output div.hatnote{padding-left:1.6em;margin-bottom:0.5em}.mw-parser-output .hatnote i{font-style:normal}.mw-parser-output .hatnote+link+.hatnote{margin-top:-0.5em}@media print{body.ns-0 .mw-parser-output .hatnote{display:none!important}}</style><div role="note" class="hatnote navigation-not-searchable">For other uses, see <a href="/wiki/NLP_(disambiguation)" class="mw-redirect mw-disambig" title="NLP (disambiguation)">NLP</a>.</div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951"><div role="note" class="hatnote navigation-not-searchable">This article is about computer processing. For human brain processing, see <a href="/wiki/Language_processing_in_the_brain" title="Language processing in the brain">Language processing in the brain</a>.</div> <style data-mw-deduplicate="TemplateStyles:r1251242444">.mw-parser-output .ambox{border:1px solid #a2a9b1;border-left:10px solid #36c;background-color:#fbfbfb;box-sizing:border-box}.mw-parser-output .ambox+link+.ambox,.mw-parser-output .ambox+link+style+.ambox,.mw-parser-output .ambox+link+link+.ambox,.mw-parser-output .ambox+.mw-empty-elt+link+.ambox,.mw-parser-output .ambox+.mw-empty-elt+link+style+.ambox,.mw-parser-output .ambox+.mw-empty-elt+link+link+.ambox{margin-top:-1px}html body.mediawiki .mw-parser-output .ambox.mbox-small-left{margin:4px 1em 4px 0;overflow:hidden;width:238px;border-collapse:collapse;font-size:88%;line-height:1.25em}.mw-parser-output .ambox-speedy{border-left:10px solid #b32424;background-color:#fee7e6}.mw-parser-output .ambox-delete{border-left:10px solid #b32424}.mw-parser-output .ambox-content{border-left:10px solid #f28500}.mw-parser-output .ambox-style{border-left:10px solid #fc3}.mw-parser-output .ambox-move{border-left:10px solid #9932cc}.mw-parser-output .ambox-protection{border-left:10px solid #a2a9b1}.mw-parser-output .ambox .mbox-text{border:none;padding:0.25em 0.5em;width:100%}.mw-parser-output .ambox .mbox-image{border:none;padding:2px 0 2px 0.5em;text-align:center}.mw-parser-output .ambox .mbox-imageright{border:none;padding:2px 0.5em 2px 0;text-align:center}.mw-parser-output .ambox .mbox-empty-cell{border:none;padding:0;width:1px}.mw-parser-output .ambox .mbox-image-div{width:52px}@media(min-width:720px){.mw-parser-output .ambox{margin:0 10%}}@media print{body.ns-0 .mw-parser-output .ambox{display:none!important}}</style><table class="box-More_citations_needed plainlinks metadata ambox ambox-content ambox-Refimprove" role="presentation"><tbody><tr><td class="mbox-text"><div class="mbox-text-span">This article needs additional citations for <a href="/wiki/Wikipedia:Verifiability" title="Wikipedia:Verifiability">verification</a>. Please help <a href="/wiki/Special:EditPage/Natural_language_processing" title="Special:EditPage/Natural language processing">improve this article</a> by <a href="/wiki/Help:Referencing_for_beginners" title="Help:Referencing for beginners">adding citations to reliable sources</a>. Unsourced material may be challenged and removed. Find sources: <a rel="nofollow" class="external text" href="https://www.google.com/search?as_eq=wikipedia&q=%22Natural+language+processing%22">"Natural language processing"</a> – <a rel="nofollow" class="external text" href="https://www.google.com/search?tbm=nws&q=%22Natural+language+processing%22+-wikipedia&tbs=ar:1">news</a> · <a rel="nofollow" class="external text" href="https://www.google.com/search?&q=%22Natural+language+processing%22&tbs=bkt:s&tbm=bks">newspapers</a> · <a rel="nofollow" class="external text" href="https://www.google.com/search?tbs=bks:1&q=%22Natural+language+processing%22+-wikipedia">books</a> · <a rel="nofollow" class="external text" href="https://scholar.google.com/scholar?q=%22Natural+language+processing%22">scholar</a> · <a rel="nofollow" class="external text" href="https://www.jstor.org/action/doBasicSearch?Query=%22Natural+language+processing%22&acc=on&wc=on">JSTOR</a> (May 2024) (<a href="/wiki/Help:Maintenance_template_removal" title="Help:Maintenance template removal">Learn how and when to remove this message</a>)</div></td></tr></tbody></table> Natural language processing (NLP) is a subfield of <a href="/wiki/Computer_science" title="Computer science">computer science</a> and especially <a href="/wiki/Artificial_intelligence" title="Artificial intelligence">artificial intelligence</a>. It is primarily concerned with providing computers with the ability to process data encoded in <a href="/wiki/Natural_language" title="Natural language">natural language</a> and is thus closely related to <a href="/wiki/Information_retrieval" title="Information retrieval">information retrieval</a>, <a href="/wiki/Knowledge_representation" class="mw-redirect" title="Knowledge representation">knowledge representation</a> and <a href="/wiki/Computational_linguistics" title="Computational linguistics">computational linguistics</a>, a subfield of <a href="/wiki/Linguistics" title="Linguistics">linguistics</a>. Typically data is collected in <a href="/wiki/Text_corpus" title="Text corpus">text corpora</a>, using either rule-based, statistical or neural-based approaches in <a href="/wiki/Machine_learning" title="Machine learning">machine learning</a> and <a href="/wiki/Deep_learning" title="Deep learning">deep learning</a>. Major tasks in natural language processing are <a href="/wiki/Speech_recognition" title="Speech recognition">speech recognition</a>, <a href="/wiki/Text_classification" class="mw-redirect" title="Text classification">text classification</a>, <a href="/wiki/Natural-language_understanding" class="mw-redirect" title="Natural-language understanding">natural-language understanding</a>, and <a href="/wiki/Natural_language_generation" title="Natural language generation">natural-language generation</a>. <div id="toc" class="toc" role="navigation" aria-labelledby="mw-toc-heading"><input type="checkbox" role="button" id="toctogglecheckbox" class="toctogglecheckbox" style="display:none"><div class="toctitle" lang="en" dir="ltr"><h2 id="mw-toc-heading">Contents</h2><label class="toctogglelabel" for="toctogglecheckbox"></label></div> <ul> <li class="toclevel-1 tocsection-1"><a href="#History">1 History</a> <ul> <li class="toclevel-2 tocsection-2"><a href="#Symbolic_NLP_(1950s_%E2%80%93_early_1990s)">1.1 Symbolic NLP (1950s – early 1990s)</a></li> <li class="toclevel-2 tocsection-3"><a href="#Statistical_NLP_(1990s%E2%80%932010s)">1.2 Statistical NLP (1990s–2010s)</a></li> <li class="toclevel-2 tocsection-4"><a href="#Neural_NLP_(present)">1.3 Neural NLP (present)</a></li> </ul> </li> <li class="toclevel-1 tocsection-5"><a href="#Approaches:_Symbolic,_statistical,_neural_networks">2 Approaches: Symbolic, statistical, neural networks</a> <ul> <li class="toclevel-2 tocsection-6"><a href="#Statistical_approach">2.1 Statistical approach</a></li> <li class="toclevel-2 tocsection-7"><a href="#Neural_networks">2.2 Neural networks</a></li> </ul> </li> <li class="toclevel-1 tocsection-8"><a href="#Common_NLP_tasks">3 Common NLP tasks</a> <ul> <li class="toclevel-2 tocsection-9"><a href="#Text_and_speech_processing">3.1 Text and speech processing</a></li> <li class="toclevel-2 tocsection-10"><a href="#Morphological_analysis">3.2 Morphological analysis</a></li> <li class="toclevel-2 tocsection-11"><a href="#Syntactic_analysis">3.3 Syntactic analysis</a></li> <li class="toclevel-2 tocsection-12"><a href="#Lexical_semantics_(of_individual_words_in_context)">3.4 Lexical semantics (of individual words in context)</a></li> <li class="toclevel-2 tocsection-13"><a href="#Relational_semantics_(semantics_of_individual_sentences)">3.5 Relational semantics (semantics of individual sentences)</a></li> <li class="toclevel-2 tocsection-14"><a href="#Discourse_(semantics_beyond_individual_sentences)">3.6 Discourse (semantics beyond individual sentences)</a></li> <li class="toclevel-2 tocsection-15"><a href="#Higher-level_NLP_applications">3.7 Higher-level NLP applications</a></li> </ul> </li> <li class="toclevel-1 tocsection-16"><a href="#General_tendencies_and_(possible)_future_directions">4 General tendencies and (possible) future directions</a> <ul> <li class="toclevel-2 tocsection-17"><a href="#Cognition">4.1 Cognition</a></li> </ul> </li> <li class="toclevel-1 tocsection-18"><a href="#See_also">5 See also</a></li> <li class="toclevel-1 tocsection-19"><a href="#References">6 References</a></li> <li class="toclevel-1 tocsection-20"><a href="#Further_reading">7 Further reading</a></li> <li class="toclevel-1 tocsection-21"><a href="#External_links">8 External links</a></li> </ul> </div> </section><div class="mw-heading mw-heading2 section-heading" onclick="mfTempOpenSection(1)"><h2 id="History">History</h2> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=1" title="Edit section: History" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div><section class="mf-section-1 collapsible-block" id="mf-section-1"> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951"><div role="note" class="hatnote navigation-not-searchable">Further information: <a href="/wiki/History_of_natural_language_processing" title="History of natural language processing">History of natural language processing</a></div> Natural language processing has its roots in the 1950s.<a href="#cite_note-1">[1]</a> Already in 1950, <a href="/wiki/Alan_Turing" title="Alan Turing">Alan Turing</a> published an article titled "<a href="/wiki/Computing_Machinery_and_Intelligence" title="Computing Machinery and Intelligence">Computing Machinery and Intelligence</a>" which proposed what is now called the <a href="/wiki/Turing_test" title="Turing test">Turing test</a> 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. <div class="mw-heading mw-heading3"><h3 id="Symbolic_NLP_(1950s_–_early_1990s)">Symbolic NLP (1950s – early 1990s)</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=2" title="Edit section: Symbolic NLP (1950s – early 1990s)" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> The premise of symbolic NLP is well-summarized by <a href="/wiki/John_Searle" title="John Searle">John Searle</a>'s <a href="/wiki/Chinese_room" title="Chinese room">Chinese room</a> 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. <ul><li>1950s: The <a href="/wiki/Georgetown-IBM_experiment" class="mw-redirect" title="Georgetown-IBM experiment">Georgetown experiment</a> in 1954 involved fully <a href="/wiki/Automatic_translation" class="mw-redirect" title="Automatic translation">automatic translation</a> of more than sixty Russian sentences into English. The authors claimed that within three or five years, machine translation would be a solved problem.<a href="#cite_note-2">[2]</a> However, real progress was much slower, and after the <a href="/wiki/ALPAC" title="ALPAC">ALPAC report</a> 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<a href="#cite_note-3">[3]</a>) until the late 1980s when the first <a href="/wiki/Statistical_machine_translation" title="Statistical machine translation">statistical machine translation</a> systems were developed.</li> <li>1960s: Some notably successful natural language processing systems developed in the 1960s were <a href="/wiki/SHRDLU" title="SHRDLU">SHRDLU</a>, a natural language system working in restricted "<a href="/wiki/Blocks_world" title="Blocks world">blocks worlds</a>" with restricted vocabularies, and <a href="/wiki/ELIZA" title="ELIZA">ELIZA</a>, a simulation of a <a href="/wiki/Rogerian_psychotherapy" class="mw-redirect" title="Rogerian psychotherapy">Rogerian psychotherapist</a>, written by <a href="/wiki/Joseph_Weizenbaum" title="Joseph Weizenbaum">Joseph Weizenbaum</a> 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?". <a href="/w/index.php?title=Ross_Quillian&action=edit&redlink=1" class="new" title="Ross Quillian (page does not exist)">Ross Quillian</a>'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.<a href="#cite_note-4">[4]</a></li></ul> <ul><li>1970s: During the 1970s, many programmers began to write "conceptual <a href="/wiki/Ontology_(information_science)" title="Ontology (information science)">ontologies</a>", 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 <a href="/wiki/Chatterbots" class="mw-redirect" title="Chatterbots">chatterbots</a> were written (e.g., <a href="/wiki/PARRY" title="PARRY">PARRY</a>).</li> <li>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 <a href="/wiki/Head-driven_phrase_structure_grammar" title="Head-driven phrase structure grammar">HPSG</a> as a computational operationalization of <a href="/wiki/Generative_grammar" title="Generative grammar">generative grammar</a>), morphology (e.g., two-level morphology<a href="#cite_note-5">[5]</a>), semantics (e.g., <a href="/wiki/Lesk_algorithm" title="Lesk algorithm">Lesk algorithm</a>), reference (e.g., within Centering Theory<a href="#cite_note-6">[6]</a>) and other areas of natural language understanding (e.g., in the <a href="/wiki/Rhetorical_structure_theory" title="Rhetorical structure theory">Rhetorical Structure Theory</a>). Other lines of research were continued, e.g., the development of chatterbots with <a href="/wiki/Racter" title="Racter">Racter</a> and <a href="/wiki/Jabberwacky" title="Jabberwacky">Jabberwacky</a>. An important development (that eventually led to the statistical turn in the 1990s) was the rising importance of quantitative evaluation in this period.<a href="#cite_note-7">[7]</a></li></ul> <div class="mw-heading mw-heading3"><h3 id="Statistical_NLP_(1990s–2010s)">Statistical NLP (1990s–2010s)</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=3" title="Edit section: Statistical NLP (1990s–2010s)" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> 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 <a href="/wiki/Machine_learning" title="Machine learning">machine learning</a> algorithms for language processing. This was due to both the steady increase in computational power (see <a href="/wiki/Moore%27s_law" title="Moore's law">Moore's law</a>) and the gradual lessening of the dominance of <a href="/wiki/Noam_Chomsky" title="Noam Chomsky">Chomskyan</a> theories of linguistics (e.g. <a href="/wiki/Transformational_grammar" title="Transformational grammar">transformational grammar</a>), whose theoretical underpinnings discouraged the sort of <a href="/wiki/Corpus_linguistics" title="Corpus linguistics">corpus linguistics</a> that underlies the machine-learning approach to language processing.<a href="#cite_note-8">[8]</a> <ul><li>1990s: Many of the notable early successes in statistical methods in NLP occurred in the field of <a href="/wiki/Machine_translation" title="Machine translation">machine translation</a>, due especially to work at IBM Research, such as <a href="/wiki/IBM_alignment_models" title="IBM alignment models">IBM alignment models</a>. These systems were able to take advantage of existing multilingual <a href="/wiki/Text_corpus" title="Text corpus">textual corpora</a> that had been produced by the <a href="/wiki/Parliament_of_Canada" title="Parliament of Canada">Parliament of Canada</a> and the <a href="/wiki/European_Union" title="European Union">European Union</a> 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.</li> <li>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 <a href="/wiki/Unsupervised_learning" title="Unsupervised learning">unsupervised</a> and <a href="/wiki/Semi-supervised_learning" class="mw-redirect" title="Semi-supervised learning">semi-supervised learning</a> 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 <a href="/wiki/Supervised_learning" title="Supervised learning">supervised learning</a>, 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 <a href="/wiki/World_Wide_Web" title="World Wide Web">World Wide Web</a>), which can often make up for the inferior results if the algorithm used has a low enough <a href="/wiki/Time_complexity" title="Time complexity">time complexity</a> to be practical.</li></ul> <div class="mw-heading mw-heading3"><h3 id="Neural_NLP_(present)">Neural NLP (present)</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=4" title="Edit section: Neural NLP (present)" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> In 2003, <a href="/wiki/Word_n-gram_language_model" title="Word n-gram language model">word n-gram model</a>, at the time the best statistical algorithm, was outperformed by a <a href="/wiki/Multi-layer_perceptron" class="mw-redirect" title="Multi-layer perceptron">multi-layer perceptron</a> (with a single hidden layer and context length of several words trained on up to 14 million of words with a CPU cluster in <a href="/wiki/Language_model" title="Language model">language modelling</a>) by <a href="/wiki/Yoshua_Bengio" title="Yoshua Bengio">Yoshua Bengio</a> with co-authors.<a href="#cite_note-9">[9]</a> In 2010, <a href="/wiki/Tom%C3%A1%C5%A1_Mikolov" title="Tomáš Mikolov">Tomáš Mikolov</a> (then a PhD student at <a href="/wiki/Brno_University_of_Technology" title="Brno University of Technology">Brno University of Technology</a>) with co-authors applied a simple <a href="/wiki/Recurrent_neural_network" title="Recurrent neural network">recurrent neural network</a> with a single hidden layer to language modelling,<a href="#cite_note-10">[10]</a> and in the following years he went on to develop <a href="/wiki/Word2vec" title="Word2vec">Word2vec</a>. In the 2010s, <a href="/wiki/Representation_learning" class="mw-redirect" title="Representation learning">representation learning</a> and <a href="/wiki/Deep_learning" title="Deep learning">deep neural network</a>-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<a href="#cite_note-goldberg:nnlp17-11">[11]</a><a href="#cite_note-goodfellow:book16-12">[12]</a> can achieve state-of-the-art results in many natural language tasks, e.g., in <a href="/wiki/Language_modeling" class="mw-redirect" title="Language modeling">language modeling</a><a href="#cite_note-jozefowicz:lm16-13">[13]</a> and parsing.<a href="#cite_note-choe:emnlp16-14">[14]</a><a href="#cite_note-vinyals:nips15-15">[15]</a> This is increasingly important <a href="/wiki/Artificial_intelligence_in_healthcare" title="Artificial intelligence in healthcare">in medicine and healthcare</a>, where NLP helps analyze notes and text in <a href="/wiki/Electronic_health_record" title="Electronic health record">electronic health records</a> that would otherwise be inaccessible for study when seeking to improve care<a href="#cite_note-16">[16]</a> or protect patient privacy.<a href="#cite_note-17">[17]</a> </section><div class="mw-heading mw-heading2 section-heading" onclick="mfTempOpenSection(2)"><h2 id="Approaches:_Symbolic,_statistical,_neural_networks">Approaches: Symbolic, statistical, neural networks</h2> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=5" title="Edit section: Approaches: Symbolic, statistical, neural networks" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div><section class="mf-section-2 collapsible-block" id="mf-section-2"> 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:<a href="#cite_note-winograd:shrdlu71-18">[18]</a><a href="#cite_note-schank77-19">[19]</a> such as by writing grammars or devising heuristic rules for <a href="/wiki/Stemming" title="Stemming">stemming</a>. <a href="/wiki/Machine_learning" title="Machine learning">Machine learning</a> approaches, which include both statistical and neural networks, on the other hand, have many advantages over the symbolic approach: <ul><li>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.</li></ul> <ul><li><a href="/wiki/Language_model" title="Language model">language models</a>, 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.</li></ul> <ul><li>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 <a href="/wiki/Intractable_problem" class="mw-redirect" title="Intractable problem">intractability</a> problems.</li></ul> Although rule-based systems for manipulating symbols were still in use in 2020, they have become mostly obsolete with the advance of <a href="/wiki/Large_language_model" title="Large language model">LLMs</a> in 2023. Before that they were commonly used: <ul><li>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 <a href="/wiki/Apertium" title="Apertium">Apertium</a> system,</li> <li>for preprocessing in NLP pipelines, e.g., <a href="/wiki/Tokenization_(lexical_analysis)" class="mw-redirect" title="Tokenization (lexical analysis)">tokenization</a>, or</li> <li>for postprocessing and transforming the output of NLP pipelines, e.g., for <a href="/wiki/Knowledge_extraction" title="Knowledge extraction">knowledge extraction</a> from syntactic parses.</li></ul> <div class="mw-heading mw-heading3"><h3 id="Statistical_approach">Statistical approach</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=6" title="Edit section: Statistical approach" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> In the late 1980s and mid-1990s, the statistical approach ended a period of <a href="/wiki/AI_winter" title="AI winter">AI winter</a>, which was caused by the inefficiencies of the rule-based approaches.<a href="#cite_note-johnson:eacl:ilcl09-20">[20]</a><a href="#cite_note-resnik:langlog11-21">[21]</a> The earliest <a href="/wiki/Decision_tree" title="Decision tree">decision trees</a>, producing systems of hard <a href="/wiki/Conditional_(computer_programming)#If%E2%80%93then(%E2%80%93else)" title="Conditional (computer programming)">if–then rules</a>, were still very similar to the old rule-based approaches. Only the introduction of hidden <a href="/wiki/Markov_model" title="Markov model">Markov models</a>, applied to part-of-speech tagging, announced the end of the old rule-based approach. <div class="mw-heading mw-heading3"><h3 id="Neural_networks">Neural networks</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=7" title="Edit section: Neural networks" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236090951"><div role="note" class="hatnote navigation-not-searchable">Further information: <a href="/wiki/Artificial_neural_network" class="mw-redirect" title="Artificial neural network">Artificial neural network</a></div> A major drawback of statistical methods is that they require elaborate <a href="/wiki/Feature_engineering" title="Feature engineering">feature engineering</a>. Since 2015,<a href="#cite_note-22">[22]</a> the statistical approach has been replaced by the <a href="/wiki/Artificial_neural_network" class="mw-redirect" title="Artificial neural network">neural networks</a> approach, using <a href="/wiki/Semantic_networks" class="mw-redirect" title="Semantic networks">semantic networks</a><a href="#cite_note-23">[23]</a> and <a href="/wiki/Word_embedding" title="Word embedding">word embeddings</a> to capture semantic properties of words. Intermediate tasks (e.g., part-of-speech tagging and dependency parsing) are not needed anymore. <a href="/wiki/Neural_machine_translation" title="Neural machine translation">Neural machine translation</a>, based on then-newly-invented <a href="/wiki/Seq2seq" title="Seq2seq">sequence-to-sequence</a> transformations, made obsolete the intermediate steps, such as word alignment, previously necessary for <a href="/wiki/Statistical_machine_translation" title="Statistical machine translation">statistical machine translation</a>. </section><div class="mw-heading mw-heading2 section-heading" onclick="mfTempOpenSection(3)"><h2 id="Common_NLP_tasks">Common NLP tasks</h2> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=8" title="Edit section: Common NLP tasks" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div><section class="mf-section-3 collapsible-block" id="mf-section-3"> 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. <div class="mw-heading mw-heading3"><h3 id="Text_and_speech_processing">Text and speech processing</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=9" title="Edit section: Text and speech processing" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> <dl><dt><a href="/wiki/Optical_character_recognition" title="Optical character recognition">Optical character recognition</a> (OCR)</dt> <dd>Given an image representing printed text, determine the corresponding text.</dd></dl> <dl><dt><a href="/wiki/Speech_recognition" title="Speech recognition">Speech recognition</a></dt> <dd>Given a sound clip of a person or people speaking, determine the textual representation of the speech. This is the opposite of <a href="/wiki/Text_to_speech" class="mw-redirect" title="Text to speech">text to speech</a> and is one of the extremely difficult problems colloquially termed "<a href="/wiki/AI-complete" title="AI-complete">AI-complete</a>" (see above). In <a href="/wiki/Natural_speech" class="mw-redirect" title="Natural speech">natural speech</a> there are hardly any pauses between successive words, and thus <a href="/wiki/Speech_segmentation" title="Speech segmentation">speech segmentation</a> 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 <a href="/wiki/Coarticulation" title="Coarticulation">coarticulation</a>, so the conversion of the <a href="/wiki/Analog_signal" title="Analog signal">analog signal</a> 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.</dd> <dt><a href="/wiki/Speech_segmentation" title="Speech segmentation">Speech segmentation</a></dt> <dd>Given a sound clip of a person or people speaking, separate it into words. A subtask of <a href="/wiki/Speech_recognition" title="Speech recognition">speech recognition</a> and typically grouped with it.</dd></dl> <dl><dt><a href="/wiki/Text-to-speech" class="mw-redirect" title="Text-to-speech">Text-to-speech</a></dt> <dd>Given a text, transform those units and produce a spoken representation. Text-to-speech can be used to aid the visually impaired.<a href="#cite_note-24">[24]</a></dd></dl> <dl><dt><a href="/wiki/Word_segmentation" class="mw-redirect" title="Word segmentation">Word segmentation</a> (<a href="/wiki/Tokenization_(lexical_analysis)" class="mw-redirect" title="Tokenization (lexical analysis)">Tokenization</a>)</dt> <dd>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.<a href="#cite_note-:0-25">[25]</a></dd> <dd>For a language like <a href="/wiki/English_language" title="English language">English</a>, this is fairly trivial, since words are usually separated by spaces. However, some written languages like <a href="/wiki/Chinese_language" title="Chinese language">Chinese</a>, <a href="/wiki/Japanese_language" title="Japanese language">Japanese</a> and <a href="/wiki/Thai_language" title="Thai language">Thai</a> do not mark word boundaries in such a fashion, and in those languages text segmentation is a significant task requiring knowledge of the <a href="/wiki/Vocabulary" title="Vocabulary">vocabulary</a> and <a href="/wiki/Morphology_(linguistics)" title="Morphology (linguistics)">morphology</a> of words in the language. Sometimes this process is also used in cases like <a href="/wiki/Bag_of_words" class="mw-redirect" title="Bag of words">bag of words</a> (BOW) creation in data mining.[<a href="/wiki/Wikipedia:Citation_needed" title="Wikipedia:Citation needed">citation needed</a>]</dd></dl> <div class="mw-heading mw-heading3"><h3 id="Morphological_analysis">Morphological analysis</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=10" title="Edit section: Morphological analysis" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> <dl><dt><a href="/wiki/Lemmatisation" class="mw-redirect" title="Lemmatisation">Lemmatization</a></dt> <dd>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.<a href="#cite_note-26">[26]</a></dd> <dt><a href="/wiki/Morphology_(linguistics)" title="Morphology (linguistics)">Morphological segmentation</a></dt> <dd>Separate words into individual <a href="/wiki/Morpheme" title="Morpheme">morphemes</a> and identify the class of the morphemes. The difficulty of this task depends greatly on the complexity of the <a href="/wiki/Morphology_(linguistics)" title="Morphology (linguistics)">morphology</a> (i.e., the structure of words) of the language being considered. <a href="/wiki/English_language" title="English language">English</a> has fairly simple morphology, especially <a href="/wiki/Inflectional_morphology" class="mw-redirect" title="Inflectional morphology">inflectional morphology</a>, 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 <a href="/wiki/Turkish_language" title="Turkish language">Turkish</a> or <a href="/wiki/Meitei_language" title="Meitei language">Meitei</a>, a highly <a href="/wiki/Agglutination" title="Agglutination">agglutinated</a> Indian language, however, such an approach is not possible, as each dictionary entry has thousands of possible word forms.<a href="#cite_note-27">[27]</a></dd> <dt><a href="/wiki/Part-of-speech_tagging" title="Part-of-speech tagging">Part-of-speech tagging</a></dt> <dd>Given a sentence, determine the <a href="/wiki/Part_of_speech" title="Part of speech">part of speech</a> (POS) for each word. Many words, especially common ones, can serve as multiple parts of speech. For example, "book" can be a <a href="/wiki/Noun" title="Noun">noun</a> ("the book on the table") or <a href="/wiki/Verb" title="Verb">verb</a> ("to book a flight"); "set" can be a noun, verb or <a href="/wiki/Adjective" title="Adjective">adjective</a>; and "out" can be any of at least five different parts of speech.</dd></dl> <dl><dt><a href="/wiki/Stemming" title="Stemming">Stemming</a></dt> <dd>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.</dd></dl> <div class="mw-heading mw-heading3"><h3 id="Syntactic_analysis">Syntactic analysis</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=11" title="Edit section: Syntactic analysis" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> <style data-mw-deduplicate="TemplateStyles:r1129693374">.mw-parser-output .hlist dl,.mw-parser-output .hlist ol,.mw-parser-output .hlist ul{margin:0;padding:0}.mw-parser-output .hlist dd,.mw-parser-output .hlist dt,.mw-parser-output .hlist li{margin:0;display:inline}.mw-parser-output .hlist.inline,.mw-parser-output .hlist.inline dl,.mw-parser-output .hlist.inline ol,.mw-parser-output .hlist.inline ul,.mw-parser-output .hlist dl dl,.mw-parser-output .hlist dl ol,.mw-parser-output .hlist dl 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.sidebar{width:22em;float:right;clear:right;margin:0.5em 0 1em 1em;background:var(--background-color-neutral-subtle,#f8f9fa);border:1px solid var(--border-color-base,#a2a9b1);padding:0.2em;text-align:center;line-height:1.4em;font-size:88%;border-collapse:collapse;display:table}body.skin-minerva .mw-parser-output .sidebar{display:table!important;float:right!important;margin:0.5em 0 1em 1em!important}.mw-parser-output .sidebar-subgroup{width:100%;margin:0;border-spacing:0}.mw-parser-output .sidebar-left{float:left;clear:left;margin:0.5em 1em 1em 0}.mw-parser-output .sidebar-none{float:none;clear:both;margin:0.5em 1em 1em 0}.mw-parser-output .sidebar-outer-title{padding:0 0.4em 0.2em;font-size:125%;line-height:1.2em;font-weight:bold}.mw-parser-output .sidebar-top-image{padding:0.4em}.mw-parser-output .sidebar-top-caption,.mw-parser-output .sidebar-pretitle-with-top-image,.mw-parser-output .sidebar-caption{padding:0.2em 0.4em 0;line-height:1.2em}.mw-parser-output .sidebar-pretitle{padding:0.4em 0.4em 0;line-height:1.2em}.mw-parser-output .sidebar-title,.mw-parser-output .sidebar-title-with-pretitle{padding:0.2em 0.8em;font-size:145%;line-height:1.2em}.mw-parser-output .sidebar-title-with-pretitle{padding:0.1em 0.4em}.mw-parser-output .sidebar-image{padding:0.2em 0.4em 0.4em}.mw-parser-output .sidebar-heading{padding:0.1em 0.4em}.mw-parser-output .sidebar-content{padding:0 0.5em 0.4em}.mw-parser-output .sidebar-content-with-subgroup{padding:0.1em 0.4em 0.2em}.mw-parser-output .sidebar-above,.mw-parser-output .sidebar-below{padding:0.3em 0.8em;font-weight:bold}.mw-parser-output .sidebar-collapse .sidebar-above,.mw-parser-output .sidebar-collapse .sidebar-below{border-top:1px solid #aaa;border-bottom:1px solid #aaa}.mw-parser-output .sidebar-navbar{text-align:right;font-size:115%;padding:0 0.4em 0.4em}.mw-parser-output .sidebar-list-title{padding:0 0.4em;text-align:left;font-weight:bold;line-height:1.6em;font-size:105%}.mw-parser-output .sidebar-list-title-c{padding:0 0.4em;text-align:center;margin:0 3.3em}@media(max-width:640px){body.mediawiki .mw-parser-output .sidebar{width:100%!important;clear:both;float:none!important;margin-left:0!important;margin-right:0!important}}body.skin--responsive .mw-parser-output .sidebar a>img{max-width:none!important}@media screen{html.skin-theme-clientpref-night .mw-parser-output .sidebar:not(.notheme) .sidebar-list-title,html.skin-theme-clientpref-night .mw-parser-output .sidebar:not(.notheme) .sidebar-title-with-pretitle{background:transparent!important}html.skin-theme-clientpref-night .mw-parser-output .sidebar:not(.notheme) .sidebar-title-with-pretitle a{color:var(--color-progressive)!important}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .sidebar:not(.notheme) .sidebar-list-title,html.skin-theme-clientpref-os .mw-parser-output .sidebar:not(.notheme) .sidebar-title-with-pretitle{background:transparent!important}html.skin-theme-clientpref-os .mw-parser-output .sidebar:not(.notheme) .sidebar-title-with-pretitle a{color:var(--color-progressive)!important}}@media print{body.ns-0 .mw-parser-output .sidebar{display:none!important}}</style> <dl><dt><a href="/wiki/Grammar_induction" title="Grammar induction">Grammar induction</a><a href="#cite_note-28">[28]</a></dt> <dd>Generate a <a href="/wiki/Formal_grammar" title="Formal grammar">formal grammar</a> that describes a language's syntax.</dd> <dt><a href="/wiki/Sentence_breaking" class="mw-redirect" title="Sentence breaking">Sentence breaking</a> (also known as "<a href="/wiki/Sentence_boundary_disambiguation" title="Sentence boundary disambiguation">sentence boundary disambiguation</a>")</dt> <dd>Given a chunk of text, find the sentence boundaries. Sentence boundaries are often marked by <a href="/wiki/Full_stop" title="Full stop">periods</a> or other <a href="/wiki/Punctuation_mark" class="mw-redirect" title="Punctuation mark">punctuation marks</a>, but these same characters can serve other purposes (e.g., marking <a href="/wiki/Abbreviation" title="Abbreviation">abbreviations</a>).</dd> <dt><a href="/wiki/Parsing" title="Parsing">Parsing</a></dt> <dd>Determine the <a href="/wiki/Parse_tree" title="Parse tree">parse tree</a> (grammatical analysis) of a given sentence. The <a href="/wiki/Grammar" title="Grammar">grammar</a> for <a href="/wiki/Natural_language" title="Natural language">natural languages</a> is <a href="/wiki/Ambiguous" class="mw-redirect" title="Ambiguous">ambiguous</a> 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 <a href="/wiki/Probabilistic_context-free_grammar" title="Probabilistic context-free grammar">probabilistic context-free grammar</a> (PCFG) (see also <a href="/wiki/Stochastic_grammar" title="Stochastic grammar">stochastic grammar</a>).</dd></dl> <div class="mw-heading mw-heading3"><h3 id="Lexical_semantics_(of_individual_words_in_context)">Lexical semantics (of individual words in context)</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=12" title="Edit section: Lexical semantics (of individual words in context)" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> <dl><dt><a href="/wiki/Lexical_semantics" title="Lexical semantics">Lexical semantics</a></dt> <dd>What is the computational meaning of individual words in context?</dd> <dt><a href="/wiki/Distributional_semantics" title="Distributional semantics">Distributional semantics</a></dt> <dd>How can we learn semantic representations from data?</dd> <dt><a href="/wiki/Named_entity_recognition" class="mw-redirect" title="Named entity recognition">Named entity recognition</a> (NER)</dt> <dd>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 <a href="/wiki/Capitalization" title="Capitalization">capitalization</a> can aid in recognizing named entities in languages such as English, this information cannot aid in determining the type of <a href="/wiki/Named_entity" title="Named entity">named entity</a>, 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. <a href="/wiki/Chinese_language" title="Chinese language">Chinese</a> or <a href="/wiki/Arabic_language" class="mw-redirect" title="Arabic language">Arabic</a>) do not have any capitalization at all, and even languages with capitalization may not consistently use it to distinguish names. For example, <a href="/wiki/German_language" title="German language">German</a> capitalizes all <a href="/wiki/Noun" title="Noun">nouns</a>, regardless of whether they are names, and <a href="/wiki/French_language" title="French language">French</a> and <a href="/wiki/Spanish_language" title="Spanish language">Spanish</a> do not capitalize names that serve as <a href="/wiki/Adjective" title="Adjective">adjectives</a>. Another name for this task is token classification.<a href="#cite_note-29">[29]</a></dd></dl> <dl><dt><a href="/wiki/Sentiment_analysis" title="Sentiment analysis">Sentiment analysis</a> (see also <a href="/wiki/Multimodal_sentiment_analysis" title="Multimodal sentiment analysis">Multimodal sentiment analysis</a>)</dt> <dd>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 <a href="/wiki/Word_n-gram_language_model" title="Word n-gram language model">word n-grams</a>, <a href="/wiki/Term_frequency-inverse_document_frequency" class="mw-redirect" title="Term frequency-inverse document frequency">Term Frequency-Inverse Document Frequency</a> (TF-IDF) features, hand-generated features, or employ <a href="/wiki/Deep_learning" title="Deep learning">deep learning</a> 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.<a href="#cite_note-:0-25">[25]</a></dd> <dt><a href="/wiki/Terminology_extraction" title="Terminology extraction">Terminology extraction</a></dt> <dd>The goal of terminology extraction is to automatically extract relevant terms from a given corpus.</dd> <dt><a href="/wiki/Word-sense_disambiguation" title="Word-sense disambiguation">Word-sense disambiguation</a> (WSD)</dt> <dd>Many words have more than one <a href="/wiki/Meaning_(linguistics)" class="mw-redirect" title="Meaning (linguistics)">meaning</a>; 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 <a href="/wiki/WordNet" title="WordNet">WordNet</a>.</dd> <dt><a href="/wiki/Entity_linking" title="Entity linking">Entity linking</a></dt> <dd>Many words—typically proper names—refer to <a href="/wiki/Named_entity" title="Named entity">named entities</a>; here we have to select the entity (a famous individual, a location, a company, etc.) which is referred to in context.</dd></dl> <div class="mw-heading mw-heading3"><h3 id="Relational_semantics_(semantics_of_individual_sentences)">Relational semantics (semantics of individual sentences)</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=13" title="Edit section: Relational semantics (semantics of individual sentences)" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> <dl><dt><a href="/wiki/Relationship_extraction" title="Relationship extraction">Relationship extraction</a></dt> <dd>Given a chunk of text, identify the relationships among named entities (e.g. who is married to whom).</dd> <dt><a href="/wiki/Semantic_parsing" title="Semantic parsing">Semantic parsing</a></dt> <dd>Given a piece of text (typically a sentence), produce a formal representation of its semantics, either as a graph (e.g., in <a href="/wiki/Abstract_Meaning_Representation" title="Abstract Meaning Representation">AMR parsing</a>) or in accordance with a logical formalism (e.g., in <a href="/wiki/Discourse_representation_theory" title="Discourse representation theory">DRT parsing</a>). 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 <a href="#Natural_language_understanding">Natural language understanding</a> below).</dd> <dt><a href="/wiki/Semantic_role_labeling" title="Semantic role labeling">Semantic role labelling</a> (see also implicit semantic role labelling below)</dt> <dd>Given a single sentence, identify and disambiguate semantic predicates (e.g., verbal <a href="/wiki/Frame_semantics_(linguistics)" title="Frame semantics (linguistics)">frames</a>), then identify and classify the frame elements (<a href="/wiki/Semantic_roles" class="mw-redirect" title="Semantic roles">semantic roles</a>).</dd></dl> <div class="mw-heading mw-heading3"><h3 id="Discourse_(semantics_beyond_individual_sentences)">Discourse (semantics beyond individual sentences)</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=14" title="Edit section: Discourse (semantics beyond individual sentences)" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> <dl><dt><a href="/wiki/Coreference" title="Coreference">Coreference resolution</a></dt> <dd>Given a sentence or larger chunk of text, determine which words ("mentions") refer to the same objects ("entities"). <a href="/wiki/Anaphora_resolution" class="mw-redirect" title="Anaphora resolution">Anaphora resolution</a> is a specific example of this task, and is specifically concerned with matching up <a href="/wiki/Pronoun" title="Pronoun">pronouns</a> with the nouns or names to which they refer. The more general task of coreference resolution also includes identifying so-called "bridging relationships" involving <a href="/wiki/Referring_expression" title="Referring expression">referring expressions</a>. 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).</dd> <dt><a href="/wiki/Discourse_analysis" title="Discourse analysis">Discourse analysis</a></dt> <dd>This rubric includes several related tasks. One task is discourse parsing, i.e., identifying the <a href="/wiki/Discourse" title="Discourse">discourse</a> 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 <a href="/wiki/Speech_act" title="Speech act">speech acts</a> in a chunk of text (e.g. yes–no question, content question, statement, assertion, etc.).</dd></dl> <dl><dt><style data-mw-deduplicate="TemplateStyles:r1238216509">.mw-parser-output .vanchor>:target~.vanchor-text{background-color:#b1d2ff}@media screen{html.skin-theme-clientpref-night .mw-parser-output .vanchor>:target~.vanchor-text{background-color:#0f4dc9}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .vanchor>:target~.vanchor-text{background-color:#0f4dc9}}</style>Implicit semantic role labelling</dt> <dd>Given a single sentence, identify and disambiguate semantic predicates (e.g., verbal <a href="/wiki/Frame_semantics_(linguistics)" title="Frame semantics (linguistics)">frames</a>) and their explicit semantic roles in the current sentence (see <a href="#Semantic_role_labelling">Semantic role labelling</a> 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 <a href="/wiki/Pro-drop_language" title="Pro-drop language">pro-drop languages</a>.</dd></dl> <dl><dt><a href="/wiki/Textual_entailment" title="Textual entailment">Recognizing textual entailment</a></dt> <dd>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.<a href="#cite_note-rte:11-30">[30]</a></dd></dl> <dl><dt><a href="/wiki/Topic_segmentation" class="mw-redirect" title="Topic segmentation">Topic segmentation</a> and recognition</dt> <dd>Given a chunk of text, separate it into segments each of which is devoted to a topic, and identify the topic of the segment.</dd></dl> <dl><dt><a href="/wiki/Argument_mining" title="Argument mining">Argument mining</a></dt> <dd>The goal of argument mining is the automatic extraction and identification of argumentative structures from <a href="/wiki/Natural_language" title="Natural language">natural language</a> text with the aid of computer programs.<a href="#cite_note-31">[31]</a> Such argumentative structures include the premise, conclusions, the <a href="/wiki/Argument_scheme" class="mw-redirect" title="Argument scheme">argument scheme</a> and the relationship between the main and subsidiary argument, or the main and counter-argument within discourse.<a href="#cite_note-32">[32]</a><a href="#cite_note-33">[33]</a></dd></dl> <div class="mw-heading mw-heading3"><h3 id="Higher-level_NLP_applications">Higher-level NLP applications</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=15" title="Edit section: Higher-level NLP applications" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> <dl><dt><a href="/wiki/Automatic_summarization" title="Automatic summarization">Automatic summarization</a> (text summarization)</dt> <dd>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.</dd> <dt><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238216509">Grammatical error correction</dt> <dd>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.<a href="#cite_note-34">[34]</a><a href="#cite_note-35">[35]</a><a href="#cite_note-36">[36]</a> 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 <a href="/wiki/GPT-2" title="GPT-2">GPT-2</a>, this can now (2019) be considered a largely solved problem and is being marketed in various commercial applications.</dd> <dt><a href="/wiki/Logic_translation" title="Logic translation">Logic translation</a></dt> <dd>Translate a text from a natural language into formal logic.</dd> <dt><a href="/wiki/Machine_translation" title="Machine translation">Machine translation</a> (MT)</dt> <dd>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 "<a href="/wiki/AI-complete" title="AI-complete">AI-complete</a>", i.e. requiring all of the different types of knowledge that humans possess (grammar, semantics, facts about the real world, etc.) to solve properly.</dd> <dt><a href="/wiki/Natural-language_understanding" class="mw-redirect" title="Natural-language understanding">Natural-language understanding</a> (NLU)</dt> <dd>Convert chunks of text into more formal representations such as <a href="/wiki/First-order_logic" title="First-order logic">first-order logic</a> structures that are easier for <a href="/wiki/Computer" title="Computer">computer</a> 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 <a href="/wiki/Closed-world_assumption" title="Closed-world assumption">closed-world assumption</a> (CWA) vs. <a href="/wiki/Open-world_assumption" class="mw-redirect" title="Open-world assumption">open-world assumption</a>, or subjective Yes/No vs. objective True/False is expected for the construction of a basis of semantics formalization.<a href="#cite_note-37">[37]</a></dd> <dt><a href="/wiki/Natural_language_generation" title="Natural language generation">Natural-language generation</a> (NLG):</dt> <dd>Convert information from computer databases or semantic intents into readable human language.</dd> <dt>Book generation</dt> <dd>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).<a href="#cite_note-38">[38]</a> The first published work by a neural network was published in 2018, <a href="/wiki/1_the_Road" title="1 the Road">1 the Road</a>, marketed as a novel, contains sixty million words. Both these systems are basically elaborate but non-sensical (semantics-free) <a href="/wiki/Language_model" title="Language model">language models</a>. The first machine-generated science book was published in 2019 (Beta Writer, Lithium-Ion Batteries, Springer, Cham).<a href="#cite_note-39">[39]</a> Unlike Racter and 1 the Road, this is grounded on factual knowledge and based on text summarization.</dd> <dt><a href="/wiki/Document_AI" title="Document AI">Document AI</a></dt> <dd>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.<a href="#cite_note-40">[40]</a></dd> <dt><a href="/wiki/Dialogue_system" title="Dialogue system">Dialogue management</a></dt> <dd>Computer systems intended to converse with a human.</dd> <dt><a href="/wiki/Question_answering" title="Question answering">Question answering</a></dt> <dd>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?").</dd> <dt><a href="/wiki/Text-to-image_generation" class="mw-redirect" title="Text-to-image generation">Text-to-image generation</a></dt> <dd>Given a description of an image, generate an image that matches the description.<a href="#cite_note-41">[41]</a></dd> <dt>Text-to-scene generation</dt> <dd>Given a description of a scene, generate a <a href="/wiki/3D_model" class="mw-redirect" title="3D model">3D model</a> of the scene.<a href="#cite_note-42">[42]</a><a href="#cite_note-43">[43]</a></dd> <dt><a href="/wiki/Text-to-video_model" title="Text-to-video model">Text-to-video</a></dt> <dd>Given a description of a video, generate a video that matches the description.<a href="#cite_note-44">[44]</a><a href="#cite_note-45">[45]</a></dd></dl> </section><div class="mw-heading mw-heading2 section-heading" onclick="mfTempOpenSection(4)"><h2 id="General_tendencies_and_(possible)_future_directions">General tendencies and (possible) future directions</h2> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=16" title="Edit section: General tendencies and (possible) future directions" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div><section class="mf-section-4 collapsible-block" id="mf-section-4"> 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:<a href="#cite_note-46">[46]</a> <ul><li>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).</li> <li>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)</li> <li>Elimination of symbolic representations (rule-based over supervised towards weakly supervised methods, representation learning and end-to-end systems)</li></ul> <div class="mw-heading mw-heading3"><h3 id="Cognition">Cognition</h3> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=17" title="Edit section: Cognition" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div> 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). <a href="/wiki/Cognition" title="Cognition">Cognition</a> refers to "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses."<a href="#cite_note-47">[47]</a> <a href="/wiki/Cognitive_science" title="Cognitive science">Cognitive science</a> is the interdisciplinary, scientific study of the mind and its processes.<a href="#cite_note-48">[48]</a> <a href="/wiki/Cognitive_linguistics" title="Cognitive linguistics">Cognitive linguistics</a> is an interdisciplinary branch of linguistics, combining knowledge and research from both psychology and linguistics.<a href="#cite_note-49">[49]</a> Especially during the age of <a href="#Symbolic_NLP_(1950s_%E2%80%93_early_1990s)">symbolic NLP</a>, the area of computational linguistics maintained strong ties with cognitive studies. As an example, <a href="/wiki/George_Lakoff" title="George Lakoff">George Lakoff</a> offers a methodology to build natural language processing (NLP) algorithms through the perspective of cognitive science, along with the findings of cognitive linguistics,<a href="#cite_note-50">[50]</a> with two defining aspects: <ol><li>Apply the theory of <a href="/wiki/Conceptual_metaphor" title="Conceptual metaphor">conceptual metaphor</a>, explained by Lakoff as "the understanding of one idea, in terms of another" which provides an idea of the intent of the author.<a href="#cite_note-51">[51]</a> 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.</li> <li>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 <a href="/wiki/Probabilistic_context-free_grammar" title="Probabilistic context-free grammar">probabilistic context-free grammar</a> (PCFG). The mathematical equation for such algorithms is presented in <a rel="nofollow" class="external text" href="https://worldwide.espacenet.com/patent/search/family/055314712/publication/US9269353B1?q=pn%3DUS9269353">US Patent 9269353</a>:<a href="#cite_note-52">[52]</a></li></ol> <dl><dd><dl><dd><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\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)}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mrow class="MJX-TeXAtom-ORD"> <mi>R</mi> <mi>M</mi> <mi>M</mi> <mo stretchy="false">(</mo> <mi>t</mi> <mi>o</mi> <mi>k</mi> <mi>e</mi> <msub> <mi>n</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>N</mi> </mrow> </msub> <mo stretchy="false">)</mo> </mrow> <mo>=</mo> <mrow class="MJX-TeXAtom-ORD"> <mi>P</mi> <mi>M</mi> <mi>M</mi> <mo stretchy="false">(</mo> <mi>t</mi> <mi>o</mi> <mi>k</mi> <mi>e</mi> <msub> <mi>n</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>N</mi> </mrow> </msub> <mo stretchy="false">)</mo> </mrow> <mo>×</mo> <mrow class="MJX-TeXAtom-ORD"> <mfrac> <mn>1</mn> <mrow> <mn>2</mn> <mi>d</mi> </mrow> </mfrac> </mrow> <mrow> <mo>(</mo> <mrow> <munderover> <mo>∑</mo> <mrow class="MJX-TeXAtom-ORD"> <mi>i</mi> <mo>=</mo> <mo>−</mo> <mi>d</mi> </mrow> <mrow class="MJX-TeXAtom-ORD"> <mi>d</mi> </mrow> </munderover> <mrow class="MJX-TeXAtom-ORD"> <mo stretchy="false">(</mo> <mo stretchy="false">(</mo> <mi>P</mi> <mi>M</mi> <mi>M</mi> <mo stretchy="false">(</mo> <mi>t</mi> <mi>o</mi> <mi>k</mi> <mi>e</mi> <msub> <mi>n</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>N</mi> </mrow> </msub> <mo stretchy="false">)</mo> </mrow> <mo>×</mo> <mrow class="MJX-TeXAtom-ORD"> <mi>P</mi> <mi>F</mi> <mo stretchy="false">(</mo> <mi>t</mi> <mi>o</mi> <mi>k</mi> <mi>e</mi> <msub> <mi>n</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>N</mi> <mo>−</mo> <mi>i</mi> </mrow> </msub> <mo>,</mo> <mi>t</mi> <mi>o</mi> <mi>k</mi> <mi>e</mi> <msub> <mi>n</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>N</mi> </mrow> </msub> <mo>,</mo> <mi>t</mi> <mi>o</mi> <mi>k</mi> <mi>e</mi> <msub> <mi>n</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>N</mi> <mo>+</mo> <mi>i</mi> </mrow> </msub> <mo stretchy="false">)</mo> <msub> <mo stretchy="false">)</mo> <mrow class="MJX-TeXAtom-ORD"> <mi>i</mi> </mrow> </msub> </mrow> </mrow> <mo>)</mo> </mrow> </mstyle> </mrow> <annotation encoding="application/x-tex">{\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)}</annotation> </semantics> </math><noscript><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/43ccadd794a4b84e20d1209997a463342e0dfbfe" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -3.171ex; width:104.427ex; height:7.509ex;" alt="{\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)}"></noscript><span class="lazy-image-placeholder" style="width: 104.427ex;height: 7.509ex;vertical-align: -3.171ex;" data-src="https://wikimedia.org/api/rest_v1/media/math/render/svg/43ccadd794a4b84e20d1209997a463342e0dfbfe" data-alt="{\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)}" data-class="mwe-math-fallback-image-inline mw-invert skin-invert"> </dd></dl></dd></dl> <dl><dd><dl><dd>Where <dl><dd>RMM is the relative measure of meaning</dd> <dd>token is any block of text, sentence, phrase or word</dd> <dd>N is the number of tokens being analyzed</dd> <dd>PMM is the probable measure of meaning based on a corpora</dd> <dd>d is the non zero location of the token along the sequence of N tokens</dd> <dd>PF is the probability function specific to a language</dd></dl></dd></dl></dd></dl> 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,<a href="#cite_note-53">[53]</a> functional grammar,<a href="#cite_note-54">[54]</a> construction grammar,<a href="#cite_note-55">[55]</a> computational psycholinguistics and cognitive neuroscience (e.g., <a href="/wiki/ACT-R" title="ACT-R">ACT-R</a>), however, with limited uptake in mainstream NLP (as measured by presence on major conferences<a href="#cite_note-56">[56]</a> of the <a href="/wiki/Association_for_Computational_Linguistics" title="Association for Computational Linguistics">ACL</a>). More recently, ideas of cognitive NLP have been revived as an approach to achieve <a href="/wiki/Explainable_artificial_intelligence" title="Explainable artificial intelligence">explainability</a>, e.g., under the notion of "cognitive AI".<a href="#cite_note-57">[57]</a> Likewise, ideas of cognitive NLP are inherent to neural models <a href="/wiki/Multimodal_interaction" title="Multimodal interaction">multimodal</a> NLP (although rarely made explicit)<a href="#cite_note-58">[58]</a> and developments in <a href="/wiki/Artificial_intelligence" title="Artificial intelligence">artificial intelligence</a>, specifically tools and technologies using <a href="/wiki/Large_language_model" title="Large language model">large language model</a> approaches<a href="#cite_note-59">[59]</a> and new directions in <a href="/wiki/Artificial_general_intelligence" title="Artificial general intelligence">artificial general intelligence</a> based on the <a href="/wiki/Free_energy_principle" title="Free energy principle">free energy principle</a><a href="#cite_note-60">[60]</a> by British neuroscientist and theoretician at University College London <a href="/wiki/Karl_J._Friston" title="Karl J. Friston">Karl J. Friston</a>. </section><div class="mw-heading mw-heading2 section-heading" onclick="mfTempOpenSection(5)"><h2 id="See_also">See also</h2> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=18" title="Edit section: See also" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div><section class="mf-section-5 collapsible-block" id="mf-section-5"> <style data-mw-deduplicate="TemplateStyles:r1184024115">.mw-parser-output .div-col{margin-top:0.3em;column-width:30em}.mw-parser-output .div-col-small{font-size:90%}.mw-parser-output .div-col-rules{column-rule:1px solid #aaa}.mw-parser-output .div-col dl,.mw-parser-output .div-col ol,.mw-parser-output .div-col ul{margin-top:0}.mw-parser-output .div-col li,.mw-parser-output .div-col dd{page-break-inside:avoid;break-inside:avoid-column}</style><div class="div-col" style="column-width: 20em;"> <ul><li><a href="/wiki/1_the_Road" title="1 the Road">1 the Road</a></li> <li><a href="/wiki/Artificial_intelligence_detection_software" class="mw-redirect" title="Artificial intelligence detection software">Artificial intelligence detection software</a></li> <li><a href="/wiki/Automated_essay_scoring" title="Automated essay scoring">Automated essay scoring</a></li> <li><a href="/wiki/Biomedical_text_mining" title="Biomedical text mining">Biomedical text mining</a></li> <li><a href="/wiki/Compound_term_processing" class="mw-redirect" title="Compound term processing">Compound term processing</a></li> <li><a href="/wiki/Computational_linguistics" title="Computational linguistics">Computational linguistics</a></li> <li><a href="/wiki/Computer-assisted_reviewing" title="Computer-assisted reviewing">Computer-assisted reviewing</a></li> <li><a href="/wiki/Controlled_natural_language" title="Controlled natural language">Controlled natural language</a></li> <li><a href="/wiki/Deep_learning" title="Deep learning">Deep learning</a></li> <li><a href="/wiki/Deep_linguistic_processing" title="Deep linguistic processing">Deep linguistic processing</a></li> <li><a href="/wiki/Distributional_semantics" title="Distributional semantics">Distributional semantics</a></li> <li><a href="/wiki/Foreign_language_reading_aid" class="mw-redirect" title="Foreign language reading aid">Foreign language reading aid</a></li> <li><a href="/wiki/Foreign_language_writing_aid" class="mw-redirect" title="Foreign language writing aid">Foreign language writing aid</a></li> <li><a href="/wiki/Information_extraction" title="Information extraction">Information extraction</a></li> <li><a href="/wiki/Information_retrieval" title="Information retrieval">Information retrieval</a></li> <li><a href="/wiki/Language_and_Communication_Technologies" title="Language and Communication Technologies">Language and Communication Technologies</a></li> <li><a href="/wiki/Language_model" title="Language model">Language model</a></li> <li><a href="/wiki/Language_technology" title="Language technology">Language technology</a></li> <li><a href="/wiki/Latent_semantic_indexing" class="mw-redirect" title="Latent semantic indexing">Latent semantic indexing</a></li> <li><a href="/wiki/Multi-agent_system" title="Multi-agent system">Multi-agent system</a></li> <li><a href="/wiki/Native-language_identification" title="Native-language identification">Native-language identification</a></li> <li><a href="/wiki/Natural-language_programming" title="Natural-language programming">Natural-language programming</a></li> <li><a href="/wiki/Natural-language_understanding" class="mw-redirect" title="Natural-language understanding">Natural-language understanding</a></li> <li><a href="/wiki/Natural-language_user_interface" title="Natural-language user interface">Natural-language search</a></li> <li><a href="/wiki/Outline_of_natural_language_processing" title="Outline of natural language processing">Outline of natural language processing</a></li> <li><a href="/wiki/Query_expansion" title="Query expansion">Query expansion</a></li> <li><a href="/wiki/Query_understanding" title="Query understanding">Query understanding</a></li> <li><a href="/wiki/Reification_(linguistics)" class="mw-redirect" title="Reification (linguistics)">Reification (linguistics)</a></li> <li><a href="/wiki/Speech_processing" title="Speech processing">Speech processing</a></li> <li><a href="/wiki/Spoken_dialogue_systems" class="mw-redirect" title="Spoken dialogue systems">Spoken dialogue systems</a></li> <li><a href="/wiki/Text-proofing" class="mw-redirect" title="Text-proofing">Text-proofing</a></li> <li><a href="/wiki/Text_simplification" title="Text simplification">Text simplification</a></li> <li><a href="/wiki/Transformer_(machine_learning_model)" class="mw-redirect" title="Transformer (machine learning model)">Transformer (machine learning model)</a></li> <li><a href="/wiki/Truecasing" title="Truecasing">Truecasing</a></li> <li><a href="/wiki/Question_answering" title="Question answering">Question answering</a></li> <li><a href="/wiki/Word2vec" title="Word2vec">Word2vec</a></li></ul> </div> </section><div class="mw-heading mw-heading2 section-heading" onclick="mfTempOpenSection(6)"><h2 id="References">References</h2> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=19" title="Edit section: References" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div><section class="mf-section-6 collapsible-block" id="mf-section-6"> <style data-mw-deduplicate="TemplateStyles:r1239543626">.mw-parser-output .reflist{margin-bottom:0.5em;list-style-type:decimal}@media screen{.mw-parser-output .reflist{font-size:90%}}.mw-parser-output .reflist .references{font-size:100%;margin-bottom:0;list-style-type:inherit}.mw-parser-output .reflist-columns-2{column-width:30em}.mw-parser-output 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The creation and use of such <a href="/wiki/Text_corpus" title="Text corpus">corpora</a> 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 "<a href="/wiki/Poverty_of_the_stimulus" title="Poverty of the stimulus">poverty of the stimulus</a>" argument entail that general learning algorithms, as are typically used in machine learning, cannot be successful in language processing. 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Journal of Diabetes Science and Technology. 15 (3): 553–560. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1177%2F19322968211000831">10.1177/19322968211000831</a>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/1932-2968">1932-2968</a>. <a href="/wiki/PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120048">8120048</a>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/33736486">33736486</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Journal+of+Diabetes+Science+and+Technology&rft.atitle=Using+Natural+Language+Processing+to+Measure+and+Improve+Quality+of+Diabetes+Care%3A+A+Systematic+Review&rft.volume=15&rft.issue=3&rft.pages=553-560&rft.date=2021-03-19&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC8120048%23id-name%3DPMC&rft.issn=1932-2968&rft_id=info%3Apmid%2F33736486&rft_id=info%3Adoi%2F10.1177%2F19322968211000831&rft.aulast=Turchin&rft.aufirst=Alexander&rft.au=Florez+Builes%2C+Luisa+F.&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC8120048&rfr_id=info%3Asid%2Fen.wikipedia.org%3ANatural+language+processing" class="Z3988"> </li> <li id="cite_note-17"><a href="#cite_ref-17">^</a> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFLeeYangHolland-HallSezgin2022" class="citation journal cs1">Lee, Jennifer; Yang, Samuel; Holland-Hall, Cynthia; Sezgin, Emre; Gill, Manjot; Linwood, Simon; Huang, Yungui; Hoffman, Jeffrey (2022-06-10). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9233261">"Prevalence of Sensitive Terms in Clinical Notes Using Natural Language Processing Techniques: Observational Study"</a>. 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Retrieved 2020-08-17.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=www.socher.org&rft.atitle=Deep+Learning+For+NLP-ACL+2012+Tutorial&rft.aulast=Socher&rft.aufirst=Richard&rft_id=https%3A%2F%2Fwww.socher.org%2Findex.php%2FMain%2FDeepLearningForNLP-ACL2012Tutorial&rfr_id=info%3Asid%2Fen.wikipedia.org%3ANatural+language+processing" class="Z3988"> 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. 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"Language models show human-like content effects on reasoning, Dasgupta, Lampinen et al". <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<a rel="nofollow" class="external text" href="https://arxiv.org/abs/2207.07051">2207.07051</a> [<a rel="nofollow" class="external text" href="https://arxiv.org/archive/cs.CL">cs.CL</a>].</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=preprint&rft.jtitle=arXiv&rft.atitle=Language+models+show+human-like+content+effects+on+reasoning%2C+Dasgupta%2C+Lampinen+et+al&rft.date=2022&rft_id=info%3Aarxiv%2F2207.07051&rft.aulast=Dasgupta&rft.aufirst=Ishita&rft.au=Lampinen%2C+Andrew+K.&rft.au=Chan%2C+Stephanie+C.+Y.&rft.au=Creswell%2C+Antonia&rft.au=Kumaran%2C+Dharshan&rft.au=McClelland%2C+James+L.&rft.au=Hill%2C+Felix&rfr_id=info%3Asid%2Fen.wikipedia.org%3ANatural+language+processing" class="Z3988"> </li> <li id="cite_note-60"><a href="#cite_ref-60">^</a> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFFriston2022" class="citation book cs1">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. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-262-36997-8" title="Special:BookSources/978-0-262-36997-8"><bdi>978-0-262-36997-8</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Active+Inference%3A+The+Free+Energy+Principle+in+Mind%2C+Brain%2C+and+Behavior%3B+Chapter+4+The+Generative+Models+of+Active+Inference&rft.pub=The+MIT+Press&rft.date=2022&rft.isbn=978-0-262-36997-8&rft.aulast=Friston&rft.aufirst=Karl+J.&rfr_id=info%3Asid%2Fen.wikipedia.org%3ANatural+language+processing" class="Z3988"> </li> </ol></div> </section><div class="mw-heading mw-heading2 section-heading" onclick="mfTempOpenSection(7)"><h2 id="Further_reading">Further reading</h2> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=20" title="Edit section: Further reading" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div><section class="mf-section-7 collapsible-block" id="mf-section-7"> <style data-mw-deduplicate="TemplateStyles:r1239549316">.mw-parser-output .refbegin{margin-bottom:0.5em}.mw-parser-output .refbegin-hanging-indents>ul{margin-left:0}.mw-parser-output .refbegin-hanging-indents>ul>li{margin-left:0;padding-left:3.2em;text-indent:-3.2em}.mw-parser-output .refbegin-hanging-indents ul,.mw-parser-output .refbegin-hanging-indents ul li{list-style:none}@media(max-width:720px){.mw-parser-output .refbegin-hanging-indents>ul>li{padding-left:1.6em;text-indent:-1.6em}}.mw-parser-output .refbegin-columns{margin-top:0.3em}.mw-parser-output .refbegin-columns ul{margin-top:0}.mw-parser-output .refbegin-columns li{page-break-inside:avoid;break-inside:avoid-column}@media screen{.mw-parser-output .refbegin{font-size:90%}}</style><div class="refbegin" style=""> <ul><li><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><cite id="CITEREFBates1995" class="citation journal cs1">Bates, M (1995). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC40721">"Models of natural language understanding"</a>. Proceedings of the National Academy of Sciences of the United States of America. 92 (22): 9977–9982. <a href="/wiki/Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/1995PNAS...92.9977B">1995PNAS...92.9977B</a>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1073%2Fpnas.92.22.9977">10.1073/pnas.92.22.9977</a>. <a href="/wiki/PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC40721">40721</a>. <a href="/wiki/PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/7479812">7479812</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America&rft.atitle=Models+of+natural+language+understanding&rft.volume=92&rft.issue=22&rft.pages=9977-9982&rft.date=1995&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC40721%23id-name%3DPMC&rft_id=info%3Apmid%2F7479812&rft_id=info%3Adoi%2F10.1073%2Fpnas.92.22.9977&rft_id=info%3Abibcode%2F1995PNAS...92.9977B&rft.aulast=Bates&rft.aufirst=M&rft_id=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC40721&rfr_id=info%3Asid%2Fen.wikipedia.org%3ANatural+language+processing" class="Z3988"></li> <li>Steven Bird, Ewan Klein, and Edward Loper (2009). Natural Language Processing with Python. O'Reilly Media. <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-596-51649-9" title="Special:BookSources/978-0-596-51649-9">978-0-596-51649-9</a>.</li> <li><a href="/w/index.php?title=Kenna_Hughes-Castleberry&action=edit&redlink=1" class="new" title="Kenna Hughes-Castleberry (page does not exist)">Kenna Hughes-Castleberry</a>, "A Murder Mystery Puzzle: The literary puzzle <a href="/wiki/Cain%27s_Jawbone" title="Cain's Jawbone">Cain's Jawbone</a>, which has stumped humans for decades, reveals the limitations of natural-language-processing algorithms", <a href="/wiki/Scientific_American" title="Scientific American">Scientific American</a>, vol. 329, no. 4 (November 2023), pp. 81–82. "This murder mystery competition has revealed that although NLP (<a href="/wiki/Natural-language_processing" class="mw-redirect" title="Natural-language processing">natural-language processing</a>) models are capable of incredible feats, their abilities are very much limited by the amount of <a href="/wiki/Context_(linguistics)" title="Context (linguistics)">context</a> they receive. This [...] could cause [difficulties] for researchers who hope to use them to do things such as analyze <a href="/wiki/Ancient_language" title="Ancient language">ancient languages</a>. In some cases, there are few historical records on long-gone <a href="/wiki/Civilization" title="Civilization">civilizations</a> to serve as <a href="/wiki/Training_data" class="mw-redirect" title="Training data">training data</a> for such a purpose." (p. 82.)</li> <li>Daniel Jurafsky and James H. Martin (2008). Speech and Language Processing, 2nd edition. Pearson Prentice Hall. <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-13-187321-6" title="Special:BookSources/978-0-13-187321-6">978-0-13-187321-6</a>.</li> <li>Mohamed Zakaria Kurdi (2016). Natural Language Processing and Computational Linguistics: speech, morphology, and syntax, Volume 1. ISTE-Wiley. <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-1848218482" title="Special:BookSources/978-1848218482">978-1848218482</a>.</li> <li>Mohamed Zakaria Kurdi (2017). Natural Language Processing and Computational Linguistics: semantics, discourse, and applications, Volume 2. ISTE-Wiley. <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-1848219212" title="Special:BookSources/978-1848219212">978-1848219212</a>.</li> <li>Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze (2008). Introduction to Information Retrieval. Cambridge University Press. <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-521-86571-5" title="Special:BookSources/978-0-521-86571-5">978-0-521-86571-5</a>. <a rel="nofollow" class="external text" href="http://nlp.stanford.edu/IR-book/">Official html and pdf versions available without charge.</a></li> <li>Christopher D. Manning and Hinrich Schütze (1999). Foundations of Statistical Natural Language Processing. The MIT Press. <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-262-13360-9" title="Special:BookSources/978-0-262-13360-9">978-0-262-13360-9</a>.</li> <li>David M. W. Powers and Christopher C. R. Turk (1989). Machine Learning of Natural Language. Springer-Verlag. <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222"><a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-0-387-19557-5" title="Special:BookSources/978-0-387-19557-5">978-0-387-19557-5</a>.</li></ul> </div> </section><div class="mw-heading mw-heading2 section-heading" onclick="mfTempOpenSection(8)"><h2 id="External_links">External links</h2> <a role="button" href="/w/index.php?title=Natural_language_processing&action=edit&section=21" title="Edit section: External links" class="cdx-button cdx-button--size-large cdx-button--fake-button cdx-button--fake-button--enabled cdx-button--icon-only cdx-button--weight-quiet "> edit </a> </div><section class="mf-section-8 collapsible-block" id="mf-section-8"> <ul><li><a href="/wiki/File:Commons-logo.svg" class="mw-file-description"><noscript><img alt="" 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.sister-bar+style+.portal-bar,.mw-parser-output .portal-bar+.navbox-styles+.navbox,.mw-parser-output .portal-bar+.navbox-styles+.sister-bar{margin-top:-1px}</style><div class="portal-bar noprint metadata noviewer portal-bar-bordered" role="navigation" aria-label="Portals"><a href="/wiki/Wikipedia:Contents/Portals" title="Wikipedia:Contents/Portals">Portal</a>:<ul class="portal-bar-content"><li class="portal-bar-item"><a href="/wiki/File:Globe_of_letters.svg" class="mw-file-description"><noscript><img alt="icon" src="//upload.wikimedia.org/wikipedia/commons/thumb/d/de/Globe_of_letters.svg/19px-Globe_of_letters.svg.png" decoding="async" width="19" height="19" class="mw-file-element" data-file-width="512" data-file-height="512"></noscript><span class="lazy-image-placeholder" style="width: 19px;height: 19px;" data-src="//upload.wikimedia.org/wikipedia/commons/thumb/d/de/Globe_of_letters.svg/19px-Globe_of_letters.svg.png" data-alt="icon" data-width="19" data-height="19" 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href="https://en.wikipedia.org/w/index.php?title=Natural_language_processing&oldid=1256458488">https://en.wikipedia.org/w/index.php?title=Natural_language_processing&oldid=1256458488</a>"</div></div> </div> <div class="post-content" id="page-secondary-actions"> </div> </main> <footer class="mw-footer minerva-footer" role="contentinfo"> <a class="last-modified-bar" href="/w/index.php?title=Natural_language_processing&action=history"> <div class="post-content last-modified-bar__content"> Last edited on 10 November 2024, at 00:59 </div> </a> <div class="post-content footer-content"> <div id='mw-data-after-content'> <div class="read-more-container"></div> </div> <div id="p-lang"> <h4>Languages</h4> <section> <ul id="p-variants" class="minerva-languages"></ul> <ul class="minerva-languages"><li class="interlanguage-link interwiki-af mw-list-item"><a href="https://af.wikipedia.org/wiki/Natuurliketaalverwerking" title="Natuurliketaalverwerking – Afrikaans" lang="af" hreflang="af" data-title="Natuurliketaalverwerking" data-language-autonym="Afrikaans" data-language-local-name="Afrikaans" class="interlanguage-link-target">Afrikaans</a></li><li class="interlanguage-link interwiki-ar mw-list-item"><a href="https://ar.wikipedia.org/wiki/%D9%85%D8%B9%D8%A7%D9%84%D8%AC%D8%A9_%D8%A7%D9%84%D9%84%D8%BA%D8%A9_%D8%A7%D9%84%D8%B7%D8%A8%D9%8A%D8%B9%D9%8A%D8%A9" title="معالجة اللغة الطبيعية – Arabic" lang="ar" hreflang="ar" data-title="معالجة اللغة الطبيعية" data-language-autonym="العربية" data-language-local-name="Arabic" class="interlanguage-link-target">العربية</a></li><li class="interlanguage-link interwiki-hyw mw-list-item"><a href="https://hyw.wikipedia.org/wiki/%D4%B2%D5%B6%D5%A1%D5%AF%D5%A1%D5%B6_%D4%BC%D5%A5%D5%A6%D5%B8%D6%82%D5%AB_%D5%84%D5%B7%D5%A1%D5%AF%D5%B8%D6%82%D5%B4" title="Բնական Լեզուի Մշակում – Western Armenian" lang="hyw" hreflang="hyw" data-title="Բնական Լեզուի Մշակում" data-language-autonym="Արեւմտահայերէն" data-language-local-name="Western Armenian" class="interlanguage-link-target">Արեւմտահայերէն</a></li><li class="interlanguage-link interwiki-az mw-list-item"><a href="https://az.wikipedia.org/wiki/T%C9%99bii_dilin_emal%C4%B1" title="Təbii dilin emalı – Azerbaijani" lang="az" hreflang="az" data-title="Təbii dilin emalı" data-language-autonym="Azərbaycanca" data-language-local-name="Azerbaijani" class="interlanguage-link-target">Azərbaycanca</a></li><li class="interlanguage-link interwiki-bn mw-list-item"><a href="https://bn.wikipedia.org/wiki/%E0%A6%B8%E0%A7%8D%E0%A6%AC%E0%A6%BE%E0%A6%AD%E0%A6%BE%E0%A6%AC%E0%A6%BF%E0%A6%95_%E0%A6%AD%E0%A6%BE%E0%A6%B7%E0%A6%BE_%E0%A6%AA%E0%A7%8D%E0%A6%B0%E0%A6%95%E0%A7%8D%E0%A6%B0%E0%A6%BF%E0%A6%AF%E0%A6%BC%E0%A6%BE%E0%A6%9C%E0%A6%BE%E0%A6%A4%E0%A6%95%E0%A6%B0%E0%A6%A3" title="স্বাভাবিক ভাষা প্রক্রিয়াজাতকরণ – Bangla" lang="bn" hreflang="bn" data-title="স্বাভাবিক ভাষা প্রক্রিয়াজাতকরণ" data-language-autonym="বাংলা" data-language-local-name="Bangla" class="interlanguage-link-target">বাংলা</a></li><li class="interlanguage-link interwiki-zh-min-nan mw-list-item"><a href="https://zh-min-nan.wikipedia.org/wiki/Ch%C5%AB-ji%C3%A2n_gi%C3%A2n-g%C3%BA_chh%C3%BA-l%C3%AD" title="Chū-jiân giân-gú chhú-lí – Minnan" lang="nan" hreflang="nan" data-title="Chū-jiân giân-gú chhú-lí" data-language-autonym="閩南語 / Bân-lâm-gú" data-language-local-name="Minnan" class="interlanguage-link-target">閩南語 / Bân-lâm-gú</a></li><li class="interlanguage-link interwiki-be mw-list-item"><a href="https://be.wikipedia.org/wiki/%D0%90%D0%BF%D1%80%D0%B0%D1%86%D0%BE%D1%9E%D0%BA%D0%B0_%D0%BD%D0%B0%D1%82%D1%83%D1%80%D0%B0%D0%BB%D1%8C%D0%BD%D0%B0%D0%B9_%D0%BC%D0%BE%D0%B2%D1%8B" title="Апрацоўка натуральнай мовы – Belarusian" lang="be" hreflang="be" data-title="Апрацоўка натуральнай мовы" data-language-autonym="Беларуская" data-language-local-name="Belarusian" class="interlanguage-link-target">Беларуская</a></li><li class="interlanguage-link interwiki-be-x-old mw-list-item"><a href="https://be-tarask.wikipedia.org/wiki/%D0%90%D0%BF%D1%80%D0%B0%D1%86%D0%BE%D1%9E%D0%BA%D0%B0_%D0%BD%D0%B0%D1%82%D1%83%D1%80%D0%B0%D0%BB%D1%8C%D0%BD%D0%B0%D0%B9_%D0%BC%D0%BE%D0%B2%D1%8B" title="Апрацоўка натуральнай мовы – Belarusian (Taraškievica orthography)" lang="be-tarask" hreflang="be-tarask" data-title="Апрацоўка натуральнай мовы" data-language-autonym="Беларуская (тарашкевіца)" data-language-local-name="Belarusian (Taraškievica orthography)" class="interlanguage-link-target">Беларуская (тарашкевіца)</a></li><li class="interlanguage-link interwiki-bg mw-list-item"><a href="https://bg.wikipedia.org/wiki/%D0%9E%D0%B1%D1%80%D0%B0%D0%B1%D0%BE%D1%82%D0%BA%D0%B0_%D0%BD%D0%B0_%D0%B5%D1%81%D1%82%D0%B5%D1%81%D1%82%D0%B2%D0%B5%D0%BD_%D0%B5%D0%B7%D0%B8%D0%BA" title="Обработка на естествен език – Bulgarian" lang="bg" hreflang="bg" data-title="Обработка на естествен език" data-language-autonym="Български" data-language-local-name="Bulgarian" class="interlanguage-link-target">Български</a></li><li class="interlanguage-link interwiki-bs mw-list-item"><a href="https://bs.wikipedia.org/wiki/Obrada_prirodnog_jezika" title="Obrada prirodnog jezika – Bosnian" lang="bs" hreflang="bs" data-title="Obrada prirodnog jezika" data-language-autonym="Bosanski" data-language-local-name="Bosnian" class="interlanguage-link-target">Bosanski</a></li><li class="interlanguage-link interwiki-br mw-list-item"><a href="https://br.wikipedia.org/wiki/Treterezh_emgefre_al_lavar" title="Treterezh emgefre al lavar – Breton" lang="br" hreflang="br" data-title="Treterezh emgefre al lavar" data-language-autonym="Brezhoneg" data-language-local-name="Breton" class="interlanguage-link-target">Brezhoneg</a></li><li class="interlanguage-link interwiki-ca mw-list-item"><a href="https://ca.wikipedia.org/wiki/Processament_del_llenguatge_natural" title="Processament del llenguatge natural – Catalan" lang="ca" hreflang="ca" data-title="Processament del llenguatge natural" data-language-autonym="Català" data-language-local-name="Catalan" class="interlanguage-link-target">Català</a></li><li class="interlanguage-link interwiki-cs mw-list-item"><a href="https://cs.wikipedia.org/wiki/Zpracov%C3%A1n%C3%AD_p%C5%99irozen%C3%A9ho_jazyka" title="Zpracování přirozeného jazyka – Czech" lang="cs" hreflang="cs" data-title="Zpracování přirozeného jazyka" data-language-autonym="Čeština" data-language-local-name="Czech" class="interlanguage-link-target">Čeština</a></li><li class="interlanguage-link interwiki-cy mw-list-item"><a href="https://cy.wikipedia.org/wiki/Prosesu_Iaith_Naturiol" title="Prosesu Iaith Naturiol – Welsh" lang="cy" hreflang="cy" data-title="Prosesu Iaith Naturiol" data-language-autonym="Cymraeg" data-language-local-name="Welsh" class="interlanguage-link-target">Cymraeg</a></li><li class="interlanguage-link interwiki-da mw-list-item"><a href="https://da.wikipedia.org/wiki/Sprogteknologi" title="Sprogteknologi – Danish" lang="da" hreflang="da" data-title="Sprogteknologi" data-language-autonym="Dansk" data-language-local-name="Danish" class="interlanguage-link-target">Dansk</a></li><li class="interlanguage-link interwiki-de badge-Q70894304 mw-list-item" title=""><a href="https://de.wikipedia.org/wiki/Verarbeitung_nat%C3%BCrlicher_Sprache" title="Verarbeitung natürlicher Sprache – German" lang="de" hreflang="de" data-title="Verarbeitung natürlicher Sprache" data-language-autonym="Deutsch" data-language-local-name="German" class="interlanguage-link-target">Deutsch</a></li><li class="interlanguage-link interwiki-et mw-list-item"><a href="https://et.wikipedia.org/wiki/Loomuliku_keele_t%C3%B6%C3%B6tlus" title="Loomuliku keele töötlus – Estonian" lang="et" hreflang="et" data-title="Loomuliku keele töötlus" data-language-autonym="Eesti" data-language-local-name="Estonian" class="interlanguage-link-target">Eesti</a></li><li class="interlanguage-link interwiki-el mw-list-item"><a href="https://el.wikipedia.org/wiki/%CE%95%CF%80%CE%B5%CE%BE%CE%B5%CF%81%CE%B3%CE%B1%CF%83%CE%AF%CE%B1_%CF%86%CF%85%CF%83%CE%B9%CE%BA%CE%AE%CF%82_%CE%B3%CE%BB%CF%8E%CF%83%CF%83%CE%B1%CF%82" title="Επεξεργασία φυσικής γλώσσας – Greek" lang="el" hreflang="el" data-title="Επεξεργασία φυσικής γλώσσας" data-language-autonym="Ελληνικά" data-language-local-name="Greek" class="interlanguage-link-target">Ελληνικά</a></li><li class="interlanguage-link interwiki-es mw-list-item"><a href="https://es.wikipedia.org/wiki/Procesamiento_de_lenguajes_naturales" title="Procesamiento de lenguajes naturales – Spanish" lang="es" hreflang="es" data-title="Procesamiento de lenguajes naturales" data-language-autonym="Español" data-language-local-name="Spanish" class="interlanguage-link-target">Español</a></li><li class="interlanguage-link interwiki-eo mw-list-item"><a href="https://eo.wikipedia.org/wiki/Natur-lingva_prilaborado" title="Natur-lingva prilaborado – Esperanto" lang="eo" hreflang="eo" data-title="Natur-lingva prilaborado" data-language-autonym="Esperanto" data-language-local-name="Esperanto" class="interlanguage-link-target">Esperanto</a></li><li class="interlanguage-link interwiki-eu mw-list-item"><a href="https://eu.wikipedia.org/wiki/Hizkuntzaren_prozesamendu" title="Hizkuntzaren prozesamendu – Basque" lang="eu" hreflang="eu" data-title="Hizkuntzaren prozesamendu" data-language-autonym="Euskara" data-language-local-name="Basque" class="interlanguage-link-target">Euskara</a></li><li class="interlanguage-link interwiki-fa mw-list-item"><a href="https://fa.wikipedia.org/wiki/%D9%BE%D8%B1%D8%AF%D8%A7%D8%B2%D8%B4_%D8%B2%D8%A8%D8%A7%D9%86%E2%80%8C%D9%87%D8%A7%DB%8C_%D8%B7%D8%A8%DB%8C%D8%B9%DB%8C" title="پردازش زبان‌های طبیعی – Persian" lang="fa" hreflang="fa" data-title="پردازش زبان‌های طبیعی" data-language-autonym="فارسی" data-language-local-name="Persian" class="interlanguage-link-target">فارسی</a></li><li class="interlanguage-link interwiki-fr mw-list-item"><a href="https://fr.wikipedia.org/wiki/Traitement_automatique_des_langues" title="Traitement automatique des langues – French" lang="fr" hreflang="fr" data-title="Traitement automatique des langues" data-language-autonym="Français" data-language-local-name="French" class="interlanguage-link-target">Français</a></li><li class="interlanguage-link interwiki-ga mw-list-item"><a href="https://ga.wikipedia.org/wiki/Pr%C3%B3ise%C3%A1il_teanga_n%C3%A1d%C3%BArtha" title="Próiseáil teanga nádúrtha – Irish" lang="ga" hreflang="ga" data-title="Próiseáil teanga nádúrtha" data-language-autonym="Gaeilge" data-language-local-name="Irish" class="interlanguage-link-target">Gaeilge</a></li><li class="interlanguage-link interwiki-gl mw-list-item"><a href="https://gl.wikipedia.org/wiki/Procesamento_da_linguaxe_natural" title="Procesamento da linguaxe natural – Galician" lang="gl" hreflang="gl" data-title="Procesamento da linguaxe natural" data-language-autonym="Galego" data-language-local-name="Galician" class="interlanguage-link-target">Galego</a></li><li class="interlanguage-link interwiki-ko mw-list-item"><a href="https://ko.wikipedia.org/wiki/%EC%9E%90%EC%97%B0%EC%96%B4_%EC%B2%98%EB%A6%AC" title="자연어 처리 – Korean" lang="ko" hreflang="ko" data-title="자연어 처리" data-language-autonym="한국어" data-language-local-name="Korean" class="interlanguage-link-target">한국어</a></li><li class="interlanguage-link interwiki-hy mw-list-item"><a href="https://hy.wikipedia.org/wiki/%D4%B2%D5%B6%D5%A1%D5%AF%D5%A1%D5%B6_%D5%AC%D5%A5%D5%A6%D5%BE%D5%AB_%D5%B4%D5%B7%D5%A1%D5%AF%D5%B8%D6%82%D5%B4" title="Բնական լեզվի մշակում – Armenian" lang="hy" hreflang="hy" data-title="Բնական լեզվի մշակում" data-language-autonym="Հայերեն" data-language-local-name="Armenian" class="interlanguage-link-target">Հայերեն</a></li><li class="interlanguage-link interwiki-hi mw-list-item"><a href="https://hi.wikipedia.org/wiki/%E0%A4%AA%E0%A5%8D%E0%A4%B0%E0%A4%BE%E0%A4%95%E0%A5%83%E0%A4%A4%E0%A4%BF%E0%A4%95_%E0%A4%AD%E0%A4%BE%E0%A4%B7%E0%A4%BE_%E0%A4%B8%E0%A4%82%E0%A4%B8%E0%A4%BE%E0%A4%A7%E0%A4%A8" title="प्राकृतिक भाषा संसाधन – Hindi" lang="hi" hreflang="hi" data-title="प्राकृतिक भाषा संसाधन" data-language-autonym="हिन्दी" data-language-local-name="Hindi" class="interlanguage-link-target">हिन्दी</a></li><li class="interlanguage-link interwiki-hr mw-list-item"><a href="https://hr.wikipedia.org/wiki/Obrada_prirodnog_jezika" title="Obrada prirodnog jezika – Croatian" lang="hr" hreflang="hr" data-title="Obrada prirodnog jezika" data-language-autonym="Hrvatski" data-language-local-name="Croatian" class="interlanguage-link-target">Hrvatski</a></li><li class="interlanguage-link interwiki-id mw-list-item"><a href="https://id.wikipedia.org/wiki/Pengolahan_bahasa_alami" title="Pengolahan bahasa alami – Indonesian" lang="id" hreflang="id" data-title="Pengolahan bahasa alami" data-language-autonym="Bahasa Indonesia" data-language-local-name="Indonesian" class="interlanguage-link-target">Bahasa Indonesia</a></li><li class="interlanguage-link interwiki-zu mw-list-item"><a href="https://zu.wikipedia.org/wiki/Ukudludlunga_ulimi_lwemvelo" title="Ukudludlunga ulimi lwemvelo – Zulu" lang="zu" hreflang="zu" data-title="Ukudludlunga ulimi lwemvelo" data-language-autonym="IsiZulu" data-language-local-name="Zulu" class="interlanguage-link-target">IsiZulu</a></li><li class="interlanguage-link interwiki-is mw-list-item"><a href="https://is.wikipedia.org/wiki/M%C3%A1lgreining" title="Málgreining – Icelandic" lang="is" hreflang="is" data-title="Málgreining" data-language-autonym="Íslenska" data-language-local-name="Icelandic" class="interlanguage-link-target">Íslenska</a></li><li class="interlanguage-link interwiki-it mw-list-item"><a href="https://it.wikipedia.org/wiki/Elaborazione_del_linguaggio_naturale" title="Elaborazione del linguaggio naturale – Italian" lang="it" hreflang="it" data-title="Elaborazione del linguaggio naturale" data-language-autonym="Italiano" data-language-local-name="Italian" class="interlanguage-link-target">Italiano</a></li><li class="interlanguage-link interwiki-he mw-list-item"><a href="https://he.wikipedia.org/wiki/%D7%A2%D7%99%D7%91%D7%95%D7%93_%D7%A9%D7%A4%D7%94_%D7%98%D7%91%D7%A2%D7%99%D7%AA" title="עיבוד שפה טבעית – Hebrew" lang="he" hreflang="he" data-title="עיבוד שפה טבעית" data-language-autonym="עברית" data-language-local-name="Hebrew" class="interlanguage-link-target">עברית</a></li><li class="interlanguage-link interwiki-kn mw-list-item"><a href="https://kn.wikipedia.org/wiki/%E0%B2%AA%E0%B3%8D%E0%B2%B0%E0%B2%BE%E0%B2%95%E0%B3%83%E0%B2%A4%E0%B2%BF%E0%B2%95_%E0%B2%AD%E0%B2%BE%E0%B2%B7%E0%B3%86%E0%B2%AF_%E0%B2%AA%E0%B2%B0%E0%B2%BF%E0%B2%B7%E0%B3%8D%E0%B2%95%E0%B2%B0%E0%B2%A3%E0%B3%86" title="ಪ್ರಾಕೃತಿಕ ಭಾಷೆಯ ಪರಿಷ್ಕರಣೆ – Kannada" lang="kn" hreflang="kn" data-title="ಪ್ರಾಕೃತಿಕ ಭಾಷೆಯ ಪರಿಷ್ಕರಣೆ" data-language-autonym="ಕನ್ನಡ" data-language-local-name="Kannada" class="interlanguage-link-target">ಕನ್ನಡ</a></li><li class="interlanguage-link interwiki-ka mw-list-item"><a href="https://ka.wikipedia.org/wiki/%E1%83%91%E1%83%A3%E1%83%9C%E1%83%94%E1%83%91%E1%83%A0%E1%83%98%E1%83%95%E1%83%98_%E1%83%94%E1%83%9C%E1%83%98%E1%83%A1_%E1%83%93%E1%83%90%E1%83%9B%E1%83%A3%E1%83%A8%E1%83%90%E1%83%95%E1%83%94%E1%83%91%E1%83%90" title="ბუნებრივი ენის დამუშავება – Georgian" lang="ka" hreflang="ka" data-title="ბუნებრივი ენის დამუშავება" data-language-autonym="ქართული" data-language-local-name="Georgian" class="interlanguage-link-target">ქართული</a></li><li class="interlanguage-link interwiki-lv mw-list-item"><a href="https://lv.wikipedia.org/wiki/Dabisk%C4%81s_valodas_apstr%C4%81de" title="Dabiskās valodas apstrāde – Latvian" lang="lv" hreflang="lv" data-title="Dabiskās valodas apstrāde" data-language-autonym="Latviešu" data-language-local-name="Latvian" class="interlanguage-link-target">Latviešu</a></li><li class="interlanguage-link interwiki-lt mw-list-item"><a href="https://lt.wikipedia.org/wiki/Nat%C5%ABralios_kalbos_apdorojimas" title="Natūralios kalbos apdorojimas – Lithuanian" lang="lt" hreflang="lt" data-title="Natūralios kalbos apdorojimas" data-language-autonym="Lietuvių" data-language-local-name="Lithuanian" class="interlanguage-link-target">Lietuvių</a></li><li class="interlanguage-link interwiki-mk mw-list-item"><a href="https://mk.wikipedia.org/wiki/%D0%9E%D0%B1%D1%80%D0%B0%D0%B1%D0%BE%D1%82%D0%BA%D0%B0_%D0%BD%D0%B0_%D0%BF%D1%80%D0%B8%D1%80%D0%BE%D0%B4%D0%BD%D0%B8_%D1%98%D0%B0%D0%B7%D0%B8%D1%86%D0%B8" title="Обработка на природни јазици – Macedonian" lang="mk" hreflang="mk" data-title="Обработка на природни јазици" data-language-autonym="Македонски" data-language-local-name="Macedonian" class="interlanguage-link-target">Македонски</a></li><li class="interlanguage-link interwiki-mr mw-list-item"><a href="https://mr.wikipedia.org/wiki/%E0%A4%A8%E0%A5%88%E0%A4%B8%E0%A4%B0%E0%A5%8D%E0%A4%97%E0%A4%BF%E0%A4%95_%E0%A4%AD%E0%A4%BE%E0%A4%B7%E0%A4%BE_%E0%A4%AA%E0%A5%8D%E0%A4%B0%E0%A4%95%E0%A5%8D%E0%A4%B0%E0%A4%BF%E0%A4%AF%E0%A4%BE" title="नैसर्गिक भाषा प्रक्रिया – Marathi" lang="mr" hreflang="mr" data-title="नैसर्गिक भाषा प्रक्रिया" data-language-autonym="मराठी" data-language-local-name="Marathi" class="interlanguage-link-target">मराठी</a></li><li class="interlanguage-link interwiki-arz mw-list-item"><a href="https://arz.wikipedia.org/wiki/%D8%AA%D8%AD%D9%84%D9%8A%D9%84_%D8%A7%D9%84%D9%84%D8%BA%D8%A7%D8%AA_%D8%A7%D9%84%D8%B7%D8%A8%D9%8A%D8%B9%D9%8A%D9%87" title="تحليل اللغات الطبيعيه – Egyptian Arabic" lang="arz" hreflang="arz" data-title="تحليل اللغات الطبيعيه" data-language-autonym="مصرى" data-language-local-name="Egyptian Arabic" class="interlanguage-link-target">مصرى</a></li><li class="interlanguage-link interwiki-mn mw-list-item"><a href="https://mn.wikipedia.org/wiki/%D0%9A%D0%BE%D0%BC%D0%BF%D1%8C%D1%8E%D1%82%D0%B5%D1%80%D1%8B%D0%BD_%D1%85%D1%8D%D0%BB_%D1%88%D0%B8%D0%BD%D0%B6%D0%BB%D1%8D%D0%BB" title="Компьютерын хэл шинжлэл – Mongolian" lang="mn" hreflang="mn" data-title="Компьютерын хэл шинжлэл" data-language-autonym="Монгол" data-language-local-name="Mongolian" class="interlanguage-link-target">Монгол</a></li><li class="interlanguage-link interwiki-my mw-list-item"><a href="https://my.wikipedia.org/wiki/%E1%80%99%E1%80%AD%E1%80%81%E1%80%84%E1%80%BA%E1%80%98%E1%80%AC%E1%80%9E%E1%80%AC%E1%80%85%E1%80%80%E1%80%AC%E1%80%B8%E1%80%9E%E1%80%AF%E1%80%B6%E1%80%B8_%E1%80%80%E1%80%BD%E1%80%94%E1%80%BA%E1%80%95%E1%80%BB%E1%80%B0%E1%80%90%E1%80%AC%E1%80%85%E1%80%94%E1%80%85%E1%80%BA" title="မိခင်ဘာသာစကားသုံး ကွန်ပျူတာစနစ် – Burmese" lang="my" hreflang="my" data-title="မိခင်ဘာသာစကားသုံး ကွန်ပျူတာစနစ်" data-language-autonym="မြန်မာဘာသာ" data-language-local-name="Burmese" class="interlanguage-link-target">မြန်မာဘာသာ</a></li><li class="interlanguage-link interwiki-ja mw-list-item"><a href="https://ja.wikipedia.org/wiki/%E8%87%AA%E7%84%B6%E8%A8%80%E8%AA%9E%E5%87%A6%E7%90%86" title="自然言語処理 – Japanese" lang="ja" hreflang="ja" data-title="自然言語処理" data-language-autonym="日本語" data-language-local-name="Japanese" class="interlanguage-link-target">日本語</a></li><li class="interlanguage-link interwiki-or mw-list-item"><a href="https://or.wikipedia.org/wiki/%E0%AC%A8%E0%AD%8D%E0%AD%9F%E0%AC%BE%E0%AC%9A%E0%AD%81%E0%AC%B0%E0%AC%BE%E0%AC%B2_%E0%AC%B2%E0%AC%BE%E0%AC%99%E0%AD%8D%E0%AC%97%E0%AD%81%E0%AC%8F%E0%AC%9C_%E0%AC%AA%E0%AD%8D%E0%AC%B0%E0%AD%8B%E0%AC%B8%E0%AD%87%E0%AC%B8%E0%AC%BF%E0%AC%82" title="ନ୍ୟାଚୁରାଲ ଲାଙ୍ଗୁଏଜ ପ୍ରୋସେସିଂ – Odia" lang="or" hreflang="or" data-title="ନ୍ୟାଚୁରାଲ ଲାଙ୍ଗୁଏଜ ପ୍ରୋସେସିଂ" data-language-autonym="ଓଡ଼ିଆ" data-language-local-name="Odia" class="interlanguage-link-target">ଓଡ଼ିଆ</a></li><li class="interlanguage-link interwiki-ps mw-list-item"><a href="https://ps.wikipedia.org/wiki/%D8%AF_%D8%B7%D8%A8%D9%8A%D8%B9%D9%8A_%DA%98%D8%A8%DB%90_%D9%BE%D8%B1%D9%88%D8%B3%D8%B3_%DA%A9%D9%88%D9%84" title="د طبيعي ژبې پروسس کول – Pashto" lang="ps" hreflang="ps" data-title="د طبيعي ژبې پروسس کول" data-language-autonym="پښتو" data-language-local-name="Pashto" class="interlanguage-link-target">پښتو</a></li><li class="interlanguage-link interwiki-pcd mw-list-item"><a href="https://pcd.wikipedia.org/wiki/Traitemint_automatique_d%27ches_langues" title="Traitemint automatique d'ches langues – Picard" lang="pcd" hreflang="pcd" data-title="Traitemint automatique d'ches langues" data-language-autonym="Picard" data-language-local-name="Picard" class="interlanguage-link-target">Picard</a></li><li class="interlanguage-link interwiki-pms mw-list-item"><a href="https://pms.wikipedia.org/wiki/NLP" title="NLP – Piedmontese" lang="pms" hreflang="pms" data-title="NLP" data-language-autonym="Piemontèis" data-language-local-name="Piedmontese" class="interlanguage-link-target">Piemontèis</a></li><li class="interlanguage-link interwiki-pl mw-list-item"><a href="https://pl.wikipedia.org/wiki/Przetwarzanie_j%C4%99zyka_naturalnego" title="Przetwarzanie języka naturalnego – Polish" lang="pl" hreflang="pl" data-title="Przetwarzanie języka naturalnego" data-language-autonym="Polski" data-language-local-name="Polish" class="interlanguage-link-target">Polski</a></li><li class="interlanguage-link interwiki-pt mw-list-item"><a href="https://pt.wikipedia.org/wiki/Processamento_de_linguagem_natural" title="Processamento de linguagem natural – Portuguese" lang="pt" hreflang="pt" data-title="Processamento de linguagem natural" data-language-autonym="Português" data-language-local-name="Portuguese" class="interlanguage-link-target">Português</a></li><li class="interlanguage-link interwiki-kaa mw-list-item"><a href="https://kaa.wikipedia.org/wiki/T%C3%A1biyiy_tildi_qayta_islew" title="Tábiyiy tildi qayta islew – Kara-Kalpak" lang="kaa" hreflang="kaa" data-title="Tábiyiy tildi qayta islew" data-language-autonym="Qaraqalpaqsha" data-language-local-name="Kara-Kalpak" class="interlanguage-link-target">Qaraqalpaqsha</a></li><li class="interlanguage-link interwiki-ro mw-list-item"><a href="https://ro.wikipedia.org/wiki/Prelucrarea_limbajului_natural" title="Prelucrarea limbajului natural – Romanian" lang="ro" hreflang="ro" data-title="Prelucrarea limbajului natural" data-language-autonym="Română" data-language-local-name="Romanian" class="interlanguage-link-target">Română</a></li><li class="interlanguage-link interwiki-qu mw-list-item"><a href="https://qu.wikipedia.org/wiki/Purum_simi_thatkichay" title="Purum simi thatkichay – Quechua" lang="qu" hreflang="qu" data-title="Purum simi thatkichay" data-language-autonym="Runa Simi" data-language-local-name="Quechua" class="interlanguage-link-target">Runa Simi</a></li><li class="interlanguage-link interwiki-ru mw-list-item"><a href="https://ru.wikipedia.org/wiki/%D0%9E%D0%B1%D1%80%D0%B0%D0%B1%D0%BE%D1%82%D0%BA%D0%B0_%D0%B5%D1%81%D1%82%D0%B5%D1%81%D1%82%D0%B2%D0%B5%D0%BD%D0%BD%D0%BE%D0%B3%D0%BE_%D1%8F%D0%B7%D1%8B%D0%BA%D0%B0" title="Обработка естественного языка – Russian" lang="ru" hreflang="ru" data-title="Обработка естественного языка" data-language-autonym="Русский" data-language-local-name="Russian" class="interlanguage-link-target">Русский</a></li><li class="interlanguage-link interwiki-sq mw-list-item"><a href="https://sq.wikipedia.org/wiki/P%C3%ABrpunimi_i_gjuh%C3%ABs_natyrore" title="Përpunimi i gjuhës natyrore – Albanian" lang="sq" hreflang="sq" data-title="Përpunimi i gjuhës natyrore" data-language-autonym="Shqip" data-language-local-name="Albanian" class="interlanguage-link-target">Shqip</a></li><li class="interlanguage-link interwiki-simple mw-list-item"><a href="https://simple.wikipedia.org/wiki/Natural_language_processing" title="Natural language processing – Simple English" lang="en-simple" hreflang="en-simple" data-title="Natural language processing" data-language-autonym="Simple English" data-language-local-name="Simple English" class="interlanguage-link-target">Simple English</a></li><li class="interlanguage-link interwiki-ckb mw-list-item"><a href="https://ckb.wikipedia.org/wiki/%D9%BE%DB%8E%D9%88%D8%A7%DA%98%DB%86%DA%A9%D8%B1%D8%AF%D9%86%DB%8C_%D8%B2%D9%85%D8%A7%D9%86%DB%8C_%D8%B3%D8%B1%D9%88%D8%B4%D8%AA%DB%8C" title="پێواژۆکردنی زمانی سروشتی – Central Kurdish" lang="ckb" hreflang="ckb" data-title="پێواژۆکردنی زمانی سروشتی" data-language-autonym="کوردی" data-language-local-name="Central Kurdish" class="interlanguage-link-target">کوردی</a></li><li class="interlanguage-link interwiki-sr mw-list-item"><a href="https://sr.wikipedia.org/wiki/Obrada_prirodnih_jezika" title="Obrada prirodnih jezika – Serbian" lang="sr" hreflang="sr" data-title="Obrada prirodnih jezika" data-language-autonym="Српски / srpski" data-language-local-name="Serbian" class="interlanguage-link-target">Српски / srpski</a></li><li class="interlanguage-link interwiki-sh mw-list-item"><a href="https://sh.wikipedia.org/wiki/Obrada_prirodnih_jezika" title="Obrada prirodnih jezika – Serbo-Croatian" lang="sh" hreflang="sh" data-title="Obrada prirodnih jezika" data-language-autonym="Srpskohrvatski / српскохрватски" data-language-local-name="Serbo-Croatian" class="interlanguage-link-target">Srpskohrvatski / српскохрватски</a></li><li class="interlanguage-link interwiki-fi mw-list-item"><a href="https://fi.wikipedia.org/wiki/Luonnollisen_kielen_k%C3%A4sittely" title="Luonnollisen kielen käsittely – Finnish" lang="fi" hreflang="fi" data-title="Luonnollisen kielen käsittely" data-language-autonym="Suomi" data-language-local-name="Finnish" class="interlanguage-link-target">Suomi</a></li><li class="interlanguage-link interwiki-ta mw-list-item"><a href="https://ta.wikipedia.org/wiki/%E0%AE%87%E0%AE%AF%E0%AE%B1%E0%AF%8D%E0%AE%95%E0%AF%88_%E0%AE%AE%E0%AF%8A%E0%AE%B4%E0%AE%BF_%E0%AE%AE%E0%AF%81%E0%AE%B1%E0%AF%88%E0%AE%AF%E0%AE%BE%E0%AE%95%E0%AF%8D%E0%AE%95%E0%AE%AE%E0%AF%8D" title="இயற்கை மொழி முறையாக்கம் – Tamil" lang="ta" hreflang="ta" data-title="இயற்கை மொழி முறையாக்கம்" data-language-autonym="தமிழ்" data-language-local-name="Tamil" class="interlanguage-link-target">தமிழ்</a></li><li class="interlanguage-link interwiki-te mw-list-item"><a href="https://te.wikipedia.org/wiki/%E0%B0%B8%E0%B0%B9%E0%B0%9C_%E0%B0%AD%E0%B0%BE%E0%B0%B7%E0%B0%BE_%E0%B0%AA%E0%B1%8D%E0%B0%B0%E0%B0%95%E0%B1%8D%E0%B0%B0%E0%B0%BF%E0%B0%AF" title="సహజ భాషా ప్రక్రియ – Telugu" lang="te" hreflang="te" data-title="సహజ భాషా ప్రక్రియ" data-language-autonym="తెలుగు" data-language-local-name="Telugu" class="interlanguage-link-target">తెలుగు</a></li><li class="interlanguage-link interwiki-th mw-list-item"><a href="https://th.wikipedia.org/wiki/%E0%B8%81%E0%B8%B2%E0%B8%A3%E0%B8%9B%E0%B8%A3%E0%B8%B0%E0%B8%A1%E0%B8%A7%E0%B8%A5%E0%B8%A0%E0%B8%B2%E0%B8%A9%E0%B8%B2%E0%B8%98%E0%B8%A3%E0%B8%A3%E0%B8%A1%E0%B8%8A%E0%B8%B2%E0%B8%95%E0%B8%B4" title="การประมวลภาษาธรรมชาติ – Thai" lang="th" hreflang="th" data-title="การประมวลภาษาธรรมชาติ" data-language-autonym="ไทย" data-language-local-name="Thai" class="interlanguage-link-target">ไทย</a></li><li class="interlanguage-link interwiki-tr mw-list-item"><a href="https://tr.wikipedia.org/wiki/Do%C4%9Fal_dil_i%C5%9Fleme" title="Doğal dil işleme – Turkish" lang="tr" hreflang="tr" data-title="Doğal dil işleme" data-language-autonym="Türkçe" data-language-local-name="Turkish" class="interlanguage-link-target">Türkçe</a></li><li class="interlanguage-link interwiki-uk mw-list-item"><a href="https://uk.wikipedia.org/wiki/%D0%9E%D0%B1%D1%80%D0%BE%D0%B1%D0%BA%D0%B0_%D0%BF%D1%80%D0%B8%D1%80%D0%BE%D0%B4%D0%BD%D0%BE%D1%97_%D0%BC%D0%BE%D0%B2%D0%B8" title="Обробка природної мови – Ukrainian" lang="uk" hreflang="uk" data-title="Обробка природної мови" data-language-autonym="Українська" data-language-local-name="Ukrainian" class="interlanguage-link-target">Українська</a></li><li class="interlanguage-link interwiki-vi mw-list-item"><a href="https://vi.wikipedia.org/wiki/X%E1%BB%AD_l%C3%BD_ng%C3%B4n_ng%E1%BB%AF_t%E1%BB%B1_nhi%C3%AAn" title="Xử lý ngôn ngữ tự nhiên – Vietnamese" lang="vi" hreflang="vi" data-title="Xử lý ngôn ngữ tự nhiên" data-language-autonym="Tiếng Việt" data-language-local-name="Vietnamese" class="interlanguage-link-target">Tiếng Việt</a></li><li class="interlanguage-link interwiki-zh-yue mw-list-item"><a href="https://zh-yue.wikipedia.org/wiki/%E8%87%AA%E7%84%B6%E8%AA%9E%E8%A8%80%E8%99%95%E7%90%86" title="自然語言處理 – Cantonese" lang="yue" hreflang="yue" data-title="自然語言處理" data-language-autonym="粵語" data-language-local-name="Cantonese" class="interlanguage-link-target">粵語</a></li><li class="interlanguage-link interwiki-zh mw-list-item"><a href="https://zh.wikipedia.org/wiki/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86" title="自然语言处理 – Chinese" lang="zh" hreflang="zh" data-title="自然语言处理" data-language-autonym="中文" data-language-local-name="Chinese" class="interlanguage-link-target">中文</a></li></ul> </section> </div> <div class="minerva-footer-logo"><img src="/static/images/mobile/copyright/wikipedia-wordmark-en.svg" alt="Wikipedia" width="120" height="18" style="width: 7.5em; height: 1.125em;"/> </div> <ul id="footer-info" class="footer-info hlist hlist-separated"> <li id="footer-info-lastmod"> This page was last edited on 10 November 2024, at 00:59 (UTC).</li> <li id="footer-info-copyright">Content is available under <a class="external" rel="nofollow" href="https://creativecommons.org/licenses/by-sa/4.0/deed.en">CC BY-SA 4.0</a> unless otherwise noted.</li> </ul> <ul id="footer-places" class="footer-places hlist hlist-separated"> <li id="footer-places-privacy"><a 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