Computational Linguistics (CL) Research Area

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A Computational Linguistics (CL) Research Area is a linguistics research area that is a computational research area (focused on computational model for natural language communication).



References

2021

2021

  • (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Computational_linguistics#Sub-fields_and_related_areas Retrieved:2021-3-23.
    • Traditionally, computational linguistics emerged as an area of artificial intelligence performed by computer scientists who had specialized in the application of computers to the processing of a natural language. With the formation of the Association for Computational Linguistics (ACL) and the establishment of independent conference series, the field consolidated during the 1970s and 1980s. The Association for Computational Linguistics defines computational linguistics as: The term "computational linguistics" is nowadays (2020) taken to be a near-synonym of natural language processing (NLP) and (human) language technology. These terms put a stronger emphasis on aspects of practical applications rather than theoretical inquiry and since the 2000s. In practice, they have largely replaced the term "computational linguistics" in the NLP/ACL community, [1] although they specifically refer to the sub-field of applied computational linguistics, only. Computational linguistics has both theoretical and applied components. Theoretical computational linguistics focuses on issues in theoretical linguistics and cognitive science. Applied computational linguistics focuses on the practical outcome of modeling human language use.
    • Theoretical computational linguistics includes the development of formal theories of grammar (parsing) and semantics, often grounded in formal logics and symbolic (knowledge-based) approaches. Areas of research that are studied by theoretical computational linguistics include:
    • Applied computational linguistics is dominated by machine learning, traditionally using statistical methods, since the mid-2010s by neural networks: Socher et al. (2012) was an early Deep Learning tutorial at the ACL 2012, and met with both interest and (at the time) scepticism 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. As for the tasks addressed by applied computational linguistics, see Natural language processing article. This includes classical problems such as the design of POS-taggers (part-of-speech taggers), parsers for natural languages, or tasks such as machine translation (MT), the sub-division of computational linguistics dealing with having computers translate between languages. As one of the earliest and most difficult applications of computational linguistics, MT draws on many subfields and both theoretical and applied aspects. Traditionally, automatic language translation has been considered a notoriously hard branch of computational linguistics. [2]
    • Aside from dichothomy between theoretical and applied computational linguistics, other divisions of computational into major areas according to different criteria exist, including:
      • medium of the language being processed, whether spoken or textual: speech recognition and speech synthesis deal with how spoken language can be understood or created using computers.
      • task being performed, e.g., whether analyzing language (recognition) or synthesizing language (generation): Parsing and generation are sub-divisions of computational linguistics dealing respectively with taking language apart and putting it together.
    • Traditionally, applications of computers to address research problems in other branches of linguistics have been described as tasks within computational linguistics. Among other aspects, this includes
  1. As pointed out, for example, by Ido Dagan at his speech at the ACL 2010 banquet in Uppsala, Sweden.
  2. Oettinger, A. G. (1965). Computational Linguistics. The American Mathematical Monthly, Vol. 72, No. 2, Part 2: Computers and Computing, pp. 147–150.

2014

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2009