Deep learning and linguistic representation / (Record no. 72914)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 04560cam a2200529Ii 4500 |
| 001 - CONTROL NUMBER | |
| control field | 9781003127086 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220531132459.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210518s2021 flu eo 000 0 eng d |
| 040 ## - Cataloging Source | |
| -- | OCoLC-P |
| -- | eng |
| -- | rda |
| -- | pn |
| -- | OCoLC-P |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781003127086 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1003127088 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781000380330 |
| -- | (electronic bk. : EPUB) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1000380335 |
| -- | (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 |
| -- | (electronic bk. : PDF) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1000380327 |
| -- | (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 - | |
| -- | COM |
| -- | 042000 |
| -- | bisacsh |
| 072 #7 - | |
| -- | COM |
| -- | 037000 |
| -- | bisacsh |
| 072 #7 - | |
| -- | COM |
| -- | 044000 |
| -- | bisacsh |
| 072 #7 - | |
| -- | CFX |
| -- | bicssc |
| 082 04 - | |
| -- | 410.285 |
| -- | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Lappin, Shalom, |
| Relator term | author. |
| 245 10 - TITLE STATEMENT | |
| Title | Deep learning and linguistic representation / |
| Statement of responsibility, etc. | Shalom Lappin. |
| 264 #1 - | |
| -- | Boca Raton, FL : |
| -- | CRC Press, |
| -- | 2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (168 pages). |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 505 0# - | |
| -- | 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 ## - | |
| -- | 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 ## - | |
| -- | OCLC-licensed vendor bibliographic record. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | COMPUTERS / Natural Language Processing |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | COMPUTERS / Machine Theory |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | COMPUTERS / Neural Networks |
| Source of heading or term | bisacsh |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computational linguistics. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Natural language processing (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning. |
| 856 40 - | |
| -- | Taylor & Francis |
| -- | https://www.taylorfrancis.com/books/9781003127086 |
| 856 42 - | |
| -- | OCLC metadata license agreement |
| -- | http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf |
No items available.