000			04379cam a22005538i 4500
001			9781003205388
003			FlBoTFG
005			20240213122826.0
006			m o d
007			cr |||||||||||
008			220920s2023 flu ob 001 0 eng
040			_aOCoLC-P _beng _erda _cOCoLC-P
020			_a9781003205388 _q(ebook)
020			_a1003205380
020			_a9781000817874 _q(electronic bk. : PDF)
020			_a1000817873 _q(electronic bk. : PDF)
020			_a9781000817881 _q(electronic bk. : EPUB)
020			_a1000817881 _q(electronic bk. : EPUB)
020			_z9781032066547 _q(hardback)
020			_z9781032071046 _q(paperback)
024	7		_a10.1201/9781003205388 _2doi
035			_a(OCoLC)1345278653
035			_a(OCoLC-P)1345278653
050	0	0	_aP118
072		7	_aCOM _x000000 _2bisacsh
072		7	_aCOM _x012040 _2bisacsh
072		7	_aCOM _x014000 _2bisacsh
072		7	_aCFX _2bicssc
082	0	0	_a401/.93 _223/eng/2020920
245	0	0	_aAlgebraic structures in natural language / _cedited by Shalom Lappin, Queen Mary University of London ; Jean-Philippe Bernardy, University of Gothenburg.
250			_aFirst edition.
264		1	_aBoca Raton : _bCRC Press, _c2023.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
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.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aLanguage acquisition.
650		0	_aMathematical linguistics.
650		0	_aDeep learning (Machine learning)
650		7	_aCOMPUTERS / General _2bisacsh
650		7	_aCOMPUTERS / Computer Graphics / Game Programming & Design _2bisacsh
650		7	_aCOMPUTERS / Computer Science _2bisacsh
700	1		_aLappin, Shalom, _eeditor.
700	1		_aBernardy, Jean-Philippe, _eeditor.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003205388
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c5047 _d5047