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Deep Learning and Linguistic Representation de Shalom Lappin

Deep Learning and Linguistic Representation: Chapman & Hall/CRC Machine Learning & Pattern Recognition

Autor Shalom Lappin
en Limba Engleză Hardback – 27 apr 2021
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.
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Specificații

ISBN-13: 9780367649470
ISBN-10: 0367649470
Pagini: 168
Ilustrații: 55
Dimensiuni: 156 x 234 x 11 mm
Greutate: 0.37 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Machine Learning & Pattern Recognition

Public țintă

Postgraduate, Professional, and Undergraduate Advanced

Cuprins

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

Notă biografică

Shalom Lappin is Professor of Natural Language Processing at Queen Mary University of London, Professor of Computational Linguistics at the University of Gothenburg and Emeritus Professor of Computational Linguistics at King’s College London.

Recenzii

This book is a very timely synthesis of classical linguistics that the author has worked in for several decades and the modern revolution in NLP enabled by Deep Learning. It also asks provocative foundational questions about whether traditional grammars are the most suitable representations of linguistic structure or if we need to go beyond them.
-- Devdatt Dubhashi, Professor, Chalmers University
Deep neural networks are having a tremendous impact on applied natural language processing. In this clearly written book, Shalom Lappin tackles the novel and exciting question of what are their implications for theories of language acquisition, representation and usage. This will be an enlightening reading for anybody interested in language from the perspectives of theoretical linguistics, cognitive science, AI and the philosophy of science.
-- Marco Baroni, ICREA Research Professor, Facebook AI Research Scientist

Descriere

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.