000			04626cam a2200541Ii 4500
001			9781003127086
003			FlBoTFG
005			20230516170546.0
006			m o d
007			cr cnu|||unuuu
008			210518s2021 flu eo 000 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781003127086 _q(electronic bk.)
020			_a1003127088 _q(electronic bk.)
020			_a9781000380330 _q(electronic bk. : EPUB)
020			_a1000380335 _q(electronic bk. : EPUB)
020			_z9780367649470
020			_z9780367648749
020			_a9781000380323 _q(electronic bk. : PDF)
020			_a1000380327 _q(electronic bk. : PDF)
035			_a(OCoLC)1251637226
035			_a(OCoLC-P)1251637226
050		4	_aP98 _b.L37 2021eb
072		7	_aCOM _x042000 _2bisacsh
072		7	_aCOM _x037000 _2bisacsh
072		7	_aCOM _x044000 _2bisacsh
072		7	_aCFX _2bicssc
082	0	4	_a410.285 _223
100	1		_aLappin, Shalom, _eauthor. _971502
245	1	0	_aDeep learning and linguistic representation / _cShalom Lappin.
264		1	_aBoca Raton, FL : _bCRC Press, _c2021.
300			_a1 online resource (168 pages).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
505	0		_aChapter 1 Introduction: Deep Learning in Natural Language Processing 1.1 OUTLINE OF THE BOOK 1.2 FROM ENGINEERING TO COGNITIVE SCIENCE 1.3 ELEMENTS OF DEEP LEARNING 1.4 TYPES OF DEEP NEURAL NETWORKS 1.5 AN EXAMPLE APPLICATION 1.6 SUMMARY AND CONCLUSIONS Chapter 2 Learning Syntactic Structure with Deep Neural Networks 2.1 SUBJECT-VERB AGREEMENT 2.2 ARCHITECTURE AND EXPERIMENTS 2.3 HIERARCHICAL STRUCTURE 2.4 TREE DNNS 2.5 SUMMARY AND CONCLUSIONS Chapter 3 Machine Learning and The Sentence Acceptability Task 3.1 GRADIENCE IN SENTENCE ACCEPTABILITY 3.2 PREDICTING ACCEPTABILITY WITH MACHINE LEARNING MODELS 3.3 ADDING TAGS AND TREES 3.4 SUMMARY AND CONCLUSIONS Chapter 4 Predicting Human Acceptability Judgments in Context4.1 ACCEPTABILITY JUDGMENTS IN CONTEXT 4.2 TWO SETS OF EXPERIMENTS 4.3 THE COMPRESSION EFFECT AND DISCOURSE COHERENCE4.4 PREDICTING ACCEPTABILITY WITH DIFFERENT DNN MODELS 4.5 SUMMARY AND CONCLUSIONS Chapter 5 Cognitively Viable Computational Models of Linguistic Knowledge 5.1 HOW USEFUL ARE LINGUISTIC THEORIES FOR NLP APPLICATIONS? 5.2 MACHINE LEARNING MODELS VS FORMAL GRAMMAR5.3 EXPLAINING LANGUAGE ACQUISITION 5.4 DEEP LEARNING AND DISTRIBUTIONAL SEMANTICS 15.5 SUMMARY AND CONCLUSIONS Chapter 6 Conclusions and Future Work 6.1 REPRESENTING SYNTACTIC AND SEMANTIC KNOWLEDGE6.2 DOMAIN SPECIFIC LEARNING BIASES AND LANGUAGE ACQUISITION 6.3 DIRECTIONS FOR FUTURE WORK REFERENCES Author IndexSubject Index
520			_aThe application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.
588			_aOCLC-licensed vendor bibliographic record.
650		7	_aCOMPUTERS / Natural Language Processing _2bisacsh _971503
650		7	_aCOMPUTERS / Machine Theory _2bisacsh _971504
650		7	_aCOMPUTERS / Neural Networks _2bisacsh _914874
650		0	_aComputational linguistics. _96146
650		0	_aNatural language processing (Computer science) _94741
650		0	_aMachine learning. _91831
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003127086
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942			_cEBK
999			_c83045 _d83045