Computational linguistics

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, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others.
Since the 2020s, computational linguistics has become a near-synonym of either natural language processing or language technology, with deep learning approaches, such as large language models, outperforming the specific approaches previously used in the field.
Origins
The field overlapped with artificial 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. Since rule-based approaches were able to make arithmetic (systematic) calculations much faster and more accurately than humans, it was expected that lexicon, morphology, syntax and semantics can be learned using explicit rules, as well. After the failure of rule-based approaches, David Hays coined the term in order to distinguish the field from AI and co-founded both the Association for Computational Linguistics (ACL) and the International 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 of natural language processing.
Annotated corpora
In order to be able to meticulously study the English language, an annotated text corpus was much needed. The Penn Treebank 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 both part-of-speech tagging and syntactic bracketing.
Japanese sentence corpora were analyzed and a pattern of log-normality was found in relation to sentence length.
Modeling language acquisition
The fact that during language acquisition, children are largely only exposed to positive evidence, meaning that the only evidence for what is a correct form is provided, and no evidence for what is not correct, was a limitation for the models at the time because the now available deep learning models were not available in late 1980s.
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, which explained the long period of language acquisition in human infants and children.
Robots have been used to test linguistic theories. Enabled to learn as children might, models were created based on an affordance 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 the Price equation and Pólya 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.
Chomsky's theories
Attempts have been made to determine how an infant learns a "non-normal grammar" as theorized by Chomsky normal form without learning an "overgeneralized version" and "getting stuck".
See also
Artificial intelligence in fiction
Collostructional analysis
Computational lexicology
Computational Linguistics (journal)
Computational models of language acquisition
Computational semantics
Computational semiotics
Computer-assisted reviewing
Dialog systems
Glottochronology
Grammar induction
Human speechome project
Internet linguistics
Lexicostatistics
Natural language processing
Natural language user interface
Quantitative linguistics
Semantic relatedness
Semantometrics
Systemic functional linguistics
Translation memory
Universal Networking Language
References
Further reading
Steven Bird, Ewan Klein, and Edward Loper (2009). Natural Language Processing with Python. O'Reilly Media. .
Daniel Jurafsky and James H. Martin (2008). Speech and Language Processing, 2nd edition. Pearson Prentice Hall. .
Mohamed Zakaria KURDI (2016). Natural Language Processing and Computational Linguistics: speech, morphology, and syntax, Volume 1. ISTE-Wiley. .
Mohamed Zakaria KURDI (2017). Natural Language Processing and Computational Linguistics: semantics, discourse, and applications, Volume 2. ISTE-Wiley. .
External links
Association for Computational Linguistics (ACL)
ACL Anthology of research papers
ACL Wiki for Computational Linguistics
CICLing annual conferences on Computational Linguistics
Computational Linguistics – Applications workshop
Language Technology World
Resources for Text, Speech and Language Processing
The Research Group in Computational Linguistics
Formal sciences
Cognitive science
Computational fields of study