Computational Linguistics (CL) Research Area

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).

• Context:
  • It can (typically) have CL Research Topic and CL Research Questions.
  • It can (typically) have CL Researchers.
  • It can (typically) have CL Conferences.
  • It can have a CL Scholarly Member Organizations, such as ACL.
  • It can range from being an Applied CL Research Area to being a Theoretical CL Research Area.
  • It can range from being a Spoken CL Research Area to being a Written CL Research Area.
  • …
• Example(s):
  • Written Language Generation Research, for written NLG.
  • …
• Counter-Example(s):
  • Natural Language Processing Research Area.
  • Cognitive Science Research Area.
  • Computational Biology.
• See: Computational Linguistics Textbook, Automated Language Generation, Automated Language Understanding.

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References

2021

• (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Computational_linguistics Retrieved:2021-3-23.
  • Computational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws upon linguistics, computer science, artificial intelligence, math, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others.

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, 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:
    • Computational complexity of natural language, largely modeled on automata theory, with the application of context-sensitive grammar and linearly bounded Turing machines.
    • Computational semantics comprises defining suitable logics for linguistic meaning representation, automatically constructing them and reasoning with them
  • 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.
  • 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
    • Computer-aided corpus linguistics, which has been used since the 1970s as a way to make detailed advances in the field of discourse analysis
    • Simulation and study of language evolution in historical linguistics/glottochronology.

2014

• https://plato.stanford.edu/entries/computational-linguistics/
  • QUOTE: Computational linguistics is the scientific and engineering discipline concerned with understanding written and spoken language from a computational perspective, and building artifacts that usefully process and produce language, either in bulk or in a dialogue setting. To the extent that language is a mirror of mind, a [[computational understanding of language also provides insight into thinking and intelligence. And since language is our most natural and most versatile means of communication, linguistically competent computers would greatly facilitate our interaction with machines and software of all sorts, and put at our fingertips, in ways that truly meet our needs, the vast textual and other resources of the internet. …

2014

• Jason Eisner http://www.quora.com/How-is-Computational-Linguistics-different-from-Natural-Language-Processing/answer/Jason-Eisner
  • QUOTE: Computational linguistics is analogous to computational biology or any other computational fill-in-the-blank. It develops computational methods to answer the scientific questions of linguistics. The core questions in linguistics involve the nature of linguistic representations and linguistic knowledge, and how linguistic knowledge is acquired and deployed in the production and comprehension of language. Answering these questions describes the human language ability and may help to explain the distribution of linguistic data and behavior that we actually observe.

    In computational linguistics, we propose formal answers to these core questions. Linguists are really asking what humans are computing and how. So we mathematically define classes of linguistic representations and formal grammars (which are usually probabilistic models nowadays) that seem adequate to capture the range of phenomena in human languages. We study their mathematical properties, and devise efficient algorithms for learning, production, and comprehension. Because the algorithms can actually run, we can test our models and find out whether they make appropriate predictions.

     Linguistics also considers a variety of questions beyond this core -- think of sociolinguistics, historical linguistics, psycholinguistics, and neurolinguistics. These scientific questions are fair game as well for computational linguists, who might use models and algorithms to make sense of the data. In this case, we are not trying to model the competence of everyday speakers in their native language, but rather to automate the special kind of reasoning that linguists do, potentially enabling us to work on bigger datasets (or even new kinds of data) and draw more accurate conclusions. Similarly, computational linguists may design software tools to help document endangered languages.

2009

• (WordNet, 2009) ⇒ http://wordnetweb.princeton.edu/perl/webwn?s=computational%20linguistics
  • S: (n) computational linguistics (the use of computers for linguistic research and applications)

• http://www.cs.cornell.edu/wya/DigLib/MS1999/Glossary.html
  • computational linguistics: The branch of natural language processing that deals with grammar and linguistics.
  • natural language processing: Use of computers to interpret and manipulate words as part of a language.

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