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Deep learning and linguistic representation

Deep learning and linguistic representation (1회 대출)

자료유형
단행본
개인저자
Lappin, Shalom, author.
서명 / 저자사항
Deep learning and linguistic representation / Shalom Lappin.
발행사항
Boca Raton :   CRC Press, Taylor & Francis Group,   2021.  
형태사항
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"--
일반주기
"A Chapman & Hall Book"--title page.  
내용주기
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.
서지주기
Includes bibliographical references (p. 123-137) and indexes.
일반주제명
Computational linguistics. Natural language processing (Computer science). Machine learning.
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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

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 410.285 L316d 등록번호 111868010 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

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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