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Deep learning and linguistic representation

Deep learning and linguistic representation (Loan 1 times)

Material type
단행본
Personal Author
Lappin, Shalom, author.
Title Statement
Deep learning and linguistic representation / Shalom Lappin.
Publication, Distribution, etc
Boca Raton :   CRC Press, Taylor & Francis Group,   2021.  
Physical Medium
xiv, 147 p. : ill. ; 25 cm.
ISBN
9780367649470 9780367648749
요약
"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"--
General Note
"A Chapman & Hall Book"--title page.  
Content Notes
Introduction: Deep learning in natural language processing -- Learning syntactic structure with deep neural networks -- Machine learning and the sentence acceptability task -- Predicting human acceptability judgments in context -- Cognitively viable computational models of linguistic knowledge -- Conclusions and future work.
Bibliography, Etc. Note
Includes bibliographical references (p. 123-137) and indexes.
Subject Added Entry-Topical Term
Computational linguistics. Natural language processing (Computer science). Machine learning.
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100 1 ▼a Lappin, Shalom, ▼e author.
245 1 0 ▼a Deep learning and linguistic representation / ▼c Shalom Lappin.
260 ▼a Boca Raton : ▼b CRC Press, Taylor & Francis Group, ▼c 2021.
264 1 ▼a Boca Raton : ▼b CRC Press, Taylor & Francis Group, ▼c 2021.
300 ▼a xiv, 147 p. : ▼b ill. ; ▼c 25 cm.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a unmediated ▼b n ▼2 rdamedia
338 ▼a volume ▼b nc ▼2 rdacarrier
500 ▼a "A Chapman & Hall Book"--title page.
504 ▼a Includes bibliographical references (p. 123-137) and indexes.
505 0 ▼a Introduction: Deep learning in natural language processing -- Learning syntactic structure with deep neural networks -- Machine learning and the sentence acceptability task -- Predicting human acceptability judgments in context -- Cognitively viable computational models of linguistic knowledge -- Conclusions and future work.
520 ▼a "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"-- ▼c Provided by publisher.
650 0 ▼a Computational linguistics.
650 0 ▼a Natural language processing (Computer science).
650 0 ▼a Machine learning.
945 ▼a ITMT

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/Western Books/ Call Number 410.285 L316d Accession No. 111868010 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

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 Context
4.1 ACCEPTABILITY JUDGMENTS IN CONTEXT
4.2 TWO SETS OF EXPERIMENTS
4.3 THE COMPRESSION EFFECT AND DISCOURSE COHERENCE
4.4 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 GRAMMAR
5.3 EXPLAINING LANGUAGE ACQUISITION
5.4 DEEP LEARNING AND DISTRIBUTIONAL SEMANTICS 1
5.5 SUMMARY AND CONCLUSIONS

Chapter 6 Conclusions and Future Work
6.1 REPRESENTING SYNTACTIC AND SEMANTIC KNOWLEDGE
6.2 DOMAIN SPECIFIC LEARNING BIASES AND LANGUAGE ACQUISITION
6.3 DIRECTIONS FOR FUTURE WORK

REFERENCES

Author Index

Subject Index

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