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Deep learning and linguistic representation / (Record no. 83045)

000 -LEADER
fixed length control field 04626cam a2200541Ii 4500
001 - CONTROL NUMBER
control field 9781003127086
003 - CONTROL NUMBER IDENTIFIER
control field FlBoTFG
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230516170546.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cnu|||unuuu
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210518s2021 flu eo 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency OCoLC-P
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency OCoLC-P
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781003127086
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1003127088
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781000380330
Qualifying information (electronic bk. : EPUB)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1000380335
Qualifying information (electronic bk. : EPUB)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9780367649470
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9780367648749
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781000380323
Qualifying information (electronic bk. : PDF)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1000380327
Qualifying information (electronic bk. : PDF)
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1251637226
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC-P)1251637226
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number P98
Item number .L37 2021eb
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM
Subject category code subdivision 042000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM
Subject category code subdivision 037000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM
Subject category code subdivision 044000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code CFX
Source bicssc
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 410.285
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Lappin, Shalom,
Relator term author.
9 (RLIN) 71502
245 10 - TITLE STATEMENT
Title Deep learning and linguistic representation /
Statement of responsibility, etc. Shalom Lappin.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Boca Raton, FL :
Name of producer, publisher, distributor, manufacturer CRC Press,
Date of production, publication, distribution, manufacture, or copyright notice 2021.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (168 pages).
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Chapter 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 ## - SUMMARY, ETC.
Summary, etc. The 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 ## - SOURCE OF DESCRIPTION NOTE
Source of description note OCLC-licensed vendor bibliographic record.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element COMPUTERS / Natural Language Processing
Source of heading or term bisacsh
9 (RLIN) 71503
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element COMPUTERS / Machine Theory
Source of heading or term bisacsh
9 (RLIN) 71504
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element COMPUTERS / Neural Networks
Source of heading or term bisacsh
9 (RLIN) 14874
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computational linguistics.
9 (RLIN) 6146
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Natural language processing (Computer science)
9 (RLIN) 4741
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
9 (RLIN) 1831
856 40 - ELECTRONIC LOCATION AND ACCESS
Materials specified Taylor & Francis
Uniform Resource Identifier <a href="https://www.taylorfrancis.com/books/9781003127086">https://www.taylorfrancis.com/books/9781003127086</a>
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified OCLC metadata license agreement
Uniform Resource Identifier <a href="http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf">http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks

No items available.