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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.
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence. It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics. Typically data is collected in text corpora, using either rule-based, statistical or neural-based approaches in machine learning and deep learning.
In linguistics, syntax is the study of how words and morphemes combine to form larger units such as phrases and sentences. Central concerns of syntax include word order, grammatical relations, hierarchical sentence structure (constituency), agreement, the nature of crosslinguistic variation, and the relationship between form and meaning (semantics). There are numerous approaches to syntax that differ in their central assumptions and goals.
Head-driven phrase structure grammar (HPSG) is a highly lexicalized, constraint-based grammar developed by Carl Pollard and Ivan Sag. It is a type of phrase structure grammar, as opposed to a dependency grammar, and it is the immediate successor to generalized phrase structure grammar. HPSG draws from other fields such as computer science and uses Ferdinand de Saussure's notion of the sign. It uses a uniform formalism and is organized in a modular way which makes it attractive for natural language processing.
Generative grammar is a research tradition in linguistics that aims to explain the cognitive basis of language by formulating and testing explicit models of humans' subconscious grammatical knowledge. Generative linguists, or generativists, tend to share certain working assumptions such as the competence–performance distinction and the notion that some domain-specific aspects of grammar are partly innate in humans. These assumptions are rejected in non-generative approaches such as usage-based models of language. Generative linguistics includes work in core areas such as syntax, semantics, phonology, psycholinguistics, and language acquisition, with additional extensions to topics including biolinguistics and music cognition.
Ivan Andrew Sag was an American linguist and cognitive scientist. He did research in areas of syntax and semantics as well as work in computational linguistics.
Paul Smolensky is Krieger-Eisenhower Professor of Cognitive Science at the Johns Hopkins University and a Senior Principal Researcher at Microsoft Research, Redmond Washington.
Joan Wanda Bresnan FBA is Sadie Dernham Patek Professor in Humanities Emerita at Stanford University. She is best known as one of the architects of the theoretical framework of lexical functional grammar.
Eva Hajičová [ˈɛva ˈɦajɪt͡ʃovaː] is a Czech linguist, specializing in topic–focus articulation and corpus linguistics. In 2006, she was awarded the Association for Computational Linguistics (ACL) Lifetime Achievement Award. She was named a fellow of the ACL in 2011.
Linguistics is the scientific study of language. The areas of linguistic analysis are syntax, semantics (meaning), morphology, phonetics, phonology, and pragmatics. Subdisciplines such as biolinguistics and psycholinguistics bridge many of these divisions.
Lauri Juhani Karttunen was an adjunct professor in linguistics at Stanford and an ACL Fellow.
The following outline is provided as an overview of and topical guide to natural-language processing:
Google Brain was a deep learning artificial intelligence research team that served as the sole AI branch of Google before being incorporated under the newer umbrella of Google AI, a research division at Google dedicated to artificial intelligence. Formed in 2011, it combined open-ended machine learning research with information systems and large-scale computing resources. It created tools such as TensorFlow, which allow neural networks to be used by the public, and multiple internal AI research projects, and aimed to create research opportunities in machine learning and natural language processing. It was merged into former Google sister company DeepMind to form Google DeepMind in April 2023.
Georgia M. Green is an American linguist and academic. She is an emeritus professor at the University of Illinois at Urbana-Champaign. Her research has focused on pragmatics, speaker intention, word order and meaning. She has been an advisory editor for several linguistics journals or publishers and she serves on the usage committee for the American Heritage Dictionary.
Dynamic Syntax (DS) is a grammar formalism and linguistic theory whose overall aim is to explain the real-time processes of language understanding and production, and describe linguistic structures as happening step-by-step over time. Under the DS approach, syntactic knowledge is understood as the ability to incrementally analyse the structure and content of spoken and written language in context and in real-time. While it posits representations similar to those used in Combinatory categorial grammars (CCG), it builds those representations left-to-right going word-by-word. Thus it differs from other syntactic models which generally abstract away from features of everyday conversation such as interruption, backtracking, and self-correction. Moreover, it differs from other approaches in that it does not postulate an independent level of syntactic structure over words.
Timnit Gebru is an Eritrean Ethiopian-born computer scientist who works in the fields of artificial intelligence (AI), algorithmic bias and data mining. She is a co-founder of Black in AI, an advocacy group that has pushed for more Black roles in AI development and research. She is the founder of the Distributed Artificial Intelligence Research Institute (DAIR).
Bonnie Jean Dorr is an American computer scientist specializing in natural language processing, machine translation, automatic summarization, social computing, and explainable artificial intelligence. She is a professor and director of the Natural Language Processing Research Laboratory in the Department of Computer & Information Science & Engineering at the University of Florida. Gainesville, Florida She is professor emerita of computer science and linguistics and former dean at the University of Maryland, College Park, former associate director at the Florida Institute for Human and Machine Cognition,, and former president of the Association for Computational Linguistics.
Margaret Mitchell is a computer scientist who works on algorithmic bias and fairness in machine learning. She is most well known for her work on automatically removing undesired biases concerning demographic groups from machine learning models, as well as more transparent reporting of their intended use.
Mona Talat Diab is a computer science professor and director of Carnegie Mellon University's Language Technologies Institute. Previously, she was a professor at George Washington University and a research scientist with Facebook AI. Her research focuses on natural language processing, computational linguistics, cross lingual/multilingual processing, computational socio-pragmatics, Arabic language processing, and applied machine learning.
In machine learning, the term stochastic parrot is a metaphor to describe the theory that large language models, though able to generate plausible language, do not understand the meaning of the language they process. The term was coined by Emily M. Bender in the 2021 artificial intelligence research paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜" by Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell.
References
- 1 2 3 Weil, Elizabeth (2023-03-01). "You Are Not a Parrot". Intelligencer. Retrieved 2023-09-11.
- ↑ "In Conversation with Emily Menon Bender - Sheila Bender's Writing It Real". 2023-09-07. Retrieved 2023-09-11.
- 1 2 3 Baković, Eric (2006-10-04). "Language Log: Speaking of missing words in American history". Language Log. Archived from the original on 2023-05-27. Retrieved 2024-02-12.
- 1 2 3 4 "Emily M. Bender". OpenReview. Retrieved 2023-09-11.
- ↑ "Emily M. Bender | Department of Linguistics | University of Washington". linguistics.washington.edu. Retrieved 2021-11-09.
- ↑ "Emily M. Bender". University of Washington faculty website. Retrieved February 4, 2023.
- ↑ Bender, Emily M. (2022-06-14). "Human-like programs abuse our empathy – even Google engineers aren't immune". The Guardian. ISSN 0261-3077 . Retrieved 2023-02-04.
- ↑ Bender, Emily. "Emily Bender CV" (PDF).
- ↑ "Emily M. Bender". University of Washington. 2021-11-10. Retrieved 2021-11-10.
- ↑ "UW Computational Linguistics Master's Degree – Online & Seattle". www.compling.uw.edu. Retrieved 2017-07-19.
- ↑ "UW Computational Linguistics Lab".
- ↑ Parvi, Joyce (2019-08-21). "Emily M. Bender is awarded Howard and Frances Nostrand Endowed Professorship for 2019–2021". linguistics.washington.edu. Retrieved 2019-12-08.
- ↑ "Emily M Bender". The Alan Turing Institute. Retrieved 2021-10-31.
- ↑ "ACL 2021 Election Results: Congratulations to Emily M. Bender and Mohit Bansal". 2021-11-09. Retrieved 2021-11-10.
- ↑ "About the ACL". 2024. Retrieved 2024-02-23.
- ↑ "ACL Officers". 2024-02-05. Retrieved 2024-02-23.
- ↑ "2022 AAAS Fellows". American Association for the Advancement of Science. Retrieved 2023-08-03.
- ↑ "Emily M. Bender: Publications". University of Washington faculty website. Retrieved 2021-11-18.
- ↑ "LinGO Grammar Matrix | Department of Linguistics | University of Washington". linguistics.washington.edu. Retrieved 2017-07-19.
- ↑ "An open source grammar development environment and broad-coverage English grammar using HPSG" (PDF). LREC. 2000. Archived from the original (PDF) on 2017-08-09. Retrieved 2017-07-19.
- ↑ Bender, Emily M.; Gebru, Timnit; McMillan-Major, Angelina; Shmitchell, Shmargaret (2021-03-03). "On the Dangers of Stochastic Parrots: Can Language Models be Too Big? 🦜". Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. FAccT '21. New York, NY, USA: Association for Computing Machinery. pp. 610–623. doi: 10.1145/3442188.3445922 . ISBN 978-1-4503-8309-7.
- ↑ Simonite, Tom. "What Really Happened When Google Ousted Timnit Gebru". Wired. ISSN 1059-1028 . Retrieved 2024-04-02.
- ↑ Hao, Karen (December 4, 2020). "We read the paper that forced Timnit Gebru out of Google. Here's what it says". MIT Technology Review.
- ↑ "Inside a Hot-Button Research Paper: Dr. Emily M. Bender Talks Large Language Models and the Future of AI Ethics". Emerging Tech Brew. Retrieved 2022-09-26.
- ↑ Bender, Emily M. (2022-05-02). "On NYT Magazine on AI: Resist the Urge to be Impressed". Medium. Retrieved 2022-09-26.
- ↑ Bender, Emily M.; Koller, Alexander (2020-07-05). "Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data". Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online: Association for Computational Linguistics: 5185–5198. doi: 10.18653/v1/2020.acl-main.463 . S2CID 211029226.
