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Computational linguistics

From Wikipedia, the free encyclopedia
Use of computational tools for the study of linguistics
This article is about the scientific field. For the journal, seeComputational Linguistics (journal).
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Computational linguistics is aninterdisciplinary field concerned with thecomputational modelling ofnatural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws uponlinguistics,computer science,artificial intelligence,mathematics,logic,philosophy,cognitive science,cognitive psychology,psycholinguistics,anthropology andneuroscience, among others. Computational linguistics is closely related tomathematical linguistics.

Origins

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The field overlapped withartificial intelligence since the efforts in the United States in the 1950s to use computers to automatically translate texts from foreign languages, particularly Russian scientific journals, into English.[1] Since rule-based approaches were able to makearithmetic (systematic) calculations much faster and more accurately than humans, it was expected thatlexicon,morphology,syntax andsemantics can be learned using explicit rules, as well. After thefailure of rule-based approaches,David Hays[2] coined the term in order to distinguish the field from AI and co-founded both theAssociation for Computational Linguistics (ACL) and theInternational Committee on Computational Linguistics (ICCL) in the 1970s and 1980s. What started as an effort to translate between languages evolved into a much wider field ofnatural language processing.[3][4]

Annotated corpora

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In order to be able to meticulously study theEnglish language, an annotated text corpus was much needed. The PennTreebank[5] was one of the most used corpora. It consisted of IBM computer manuals, transcribed telephone conversations, and other texts, together containing over 4.5 million words of American English, annotated using bothpart-of-speech tagging and syntactic bracketing.[6]

Japanese sentence corpora were analyzed and a pattern oflog-normality was found in relation to sentence length.[7]

Modeling language acquisition

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The fact that duringlanguage acquisition, children are largely only exposed to positive evidence,[8] meaning that the only evidence for what is a correct form is provided, and no evidence for what is not correct,[9] was a limitation for the models at the time because the now availabledeep learning models were not available in late 1980s.[10]

It has been shown that languages can be learned with a combination of simple input presented incrementally as the child develops better memory and longer attention span,[11] which explained the long period oflanguage acquisition in human infants and children.[11]

Robots have been used to test linguistic theories.[12] Enabled to learn as children might, models were created based on anaffordance model in which mappings between actions, perceptions, and effects were created and linked to spoken words. Crucially, these robots were able to acquire functioning word-to-meaning mappings without needing grammatical structure.

Using thePrice equation andPólya urn dynamics, researchers have created a system which not only predicts future linguistic evolution but also gives insight into the evolutionary history of modern-day languages.[13]

Chomsky's theories

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Noam Chomsky's theories have influenced computational linguistics, particularly in understanding how infants learn complex grammatical structures, such as those described inChomsky normal form.[14] Attempts have been made to determine how an infant learns a "non-normal grammar" as theorized by Chomsky normal form.[9] Research in this area combines structural approaches with computational models to analyze largelinguistic corpora like the PennTreebank, helping to uncover patterns in language acquisition.[15]

Software

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See also:List of linguistic research software

See also

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References

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  1. ^John Hutchins:Retrospect and prospect in computer-based translation.Archived 2008-04-14 at theWayback Machine Proceedings of MT Summit VII, 1999, pp. 30–44.
  2. ^"Deceased members".ICCL members. Archived fromthe original on 17 May 2017. Retrieved15 November 2017.
  3. ^Natural Language Processing by Liz Liddy, Eduard Hovy, Jimmy Lin, John Prager, Dragomir Radev, Lucy Vanderwende, Ralph Weischedel
  4. ^Arnold B. Barach:Translating Machine 1975: And the Changes To Come.
  5. ^Marcus, M. & Marcinkiewicz, M. (1993)."Building a large annotated corpus of English: The Penn Treebank"(PDF).Computational Linguistics.19 (2):313–330.Archived(PDF) from the original on 2022-10-09.
  6. ^Taylor, Ann (2003). "1".Treebanks. Spring Netherlands. pp. 5–22.
  7. ^Furuhashi, S. & Hayakawa, Y. (2012). "Lognormality of the Distribution of Japanese Sentence Lengths".Journal of the Physical Society of Japan.81 (3) 034004.Bibcode:2012JPSJ...81c4004F.doi:10.1143/JPSJ.81.034004.
  8. ^Bowerman, M. (1988).The "no negative evidence" problem: How do children avoid constructing an overly general grammar. Explaining language universals.
  9. ^abBraine, M.D.S. (1971). On two types of models of the internalization of grammars. In D.I. Slobin (Ed.), The ontogenesis of grammar: A theoretical perspective. New York: Academic Press.
  10. ^Powers, D.M.W. & Turk, C.C.R. (1989).Machine Learning of Natural Language. Springer-Verlag.ISBN 978-0-387-19557-5.
  11. ^abElman, Jeffrey L. (1993). "Learning and development in neural networks: The importance of starting small".Cognition.48 (1):71–99.CiteSeerX 10.1.1.135.4937.doi:10.1016/0010-0277(93)90058-4.PMID 8403835.S2CID 2105042.
  12. ^Salvi, G.; Montesano, L.; Bernardino, A.; Santos-Victor, J. (2012). "Language bootstrapping: learning word meanings from the perception-action association".IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics.42 (3):660–71.arXiv:1711.09714.Bibcode:2012ITSMC..42..660S.doi:10.1109/TSMCB.2011.2172420.PMID 22106152.S2CID 977486.
  13. ^Gong, T.; Shuai, L.; Tamariz, M. & Jäger, G. (2012). E. Scalas (ed.)."Studying Language Change Using Price Equation and Pólya-urn Dynamics".PLOS ONE.7 (3) e33171.Bibcode:2012PLoSO...733171G.doi:10.1371/journal.pone.0033171.PMC 3299756.PMID 22427981.
  14. ^Yogita, Bansal (2016)."Insight to Computational Linguistics"(PDF). International Journal 4.10. p. 94. RetrievedSeptember 22, 2024.
  15. ^Yogita, Bansal (2016)."Insight to Computational Linguistics"(PDF). International Journal 4.10. p. 94. RetrievedSeptember 22, 2024.

Further reading

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External links

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Wikiversity has learning resources aboutComputational linguistics
Wikimedia Commons has media related toComputational linguistics.
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