000			03808cam a22004098i 4500
001			22795988
003			OSt
005			20250222122357.0
008			220920s2023 flu b 001 0 eng
010			_a 2022031217
020			_a9781032066547 (hbk.)
040			_aDLC _beng _erda _cJKRC
082	0	0	_a401.93 _223 _bLAP.S
245	0	0	_aAlgebraic structures in natural language / _ced by Shalom Lappin and Jean-Philippe Bernardy. _hEnglish.
250			_a1st ed.
260			_aBoca Raton : _bCRC Press, _c2023.
263			_a2212
300			_axviii, 290 p. _c26 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
520			_a"Algebraic Structures in Natural Language addresses a central problem in cognitive science, concerning the learning procedures through which humans acquire and represent natural language. Until recently algebraic systems have dominated the study of natural language in formal and computational linguistics, AI, and the of psychology of language, with linguistic knowledge seen as encoded in formal grammars, model theories, proof theories, and other rule driven devices, and researchers drawing conclusions about how humans acquire and represent language. Recent work on deep learning has produced an increasingly powerful set of general learning mechanisms which do not apply algebraic models of representation (although they can be combined with them), and success in NLP in particular has led some researchers to question the role of algebraic models in the study of human language acquisition and linguistic representation. Psychologists and cognitive scientists have also been exploring explanations of language evolution and language acquisition that rely on probabilistic methods, social interaction, and information theory, rather than on formal models of grammar induction. This work has also led some researchers to question the centrality of algebraic approaches to linguistic representation. This book addresses the learning procedures through which humans acquire natural language, and the way in which they represent its properties. It brings together leading researchers from computational linguistics, psychology, behavioural science, and mathematical linguistics to consider the significance of non-algebraic methods for the study of natural language, and represents a wide spectrum of views, from the claim that algebraic systems are largely irrelevant, to the contrary position that non-algebraic learning methods are engineering devices for efficiently identifying the patterns that underlying grammars and semantic models generate for natural language input. There are interesting and important perspectives that fall at intermediate points between these opposing approaches, and they may combine elements of both. It will appeal to researchers and advanced students in each of these fields, as well as to anyone who wants to learn more about the relationship between algorithms and language"-- _cProvided by publisher.
650		0	_aLanguage acquisition. _931304
650		0	_aMathematical linguistics. _931305
650		0	_aDeep learning (Machine learning) _931306
653			_aMathematical linguistics.
653			_aLanguage acquisition.
653			_aDeep learning (Machine learning)
655		7	_aEssays. _2lcgft _931307
700	1		_aLappin, Shalom. _eeditor. _931308
700	1		_aBernardy, Jean-Philippe. _eeditor. _931309
776	0	8	_iOnline version: _tAlgebraic structures in natural language. _bFirst edition _dBoca Raton : CRC Press, 2023 _z9781003205388 _w(DLC) 2022031218
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _c1 _e23 _n0
999			_c429002 _d429002