Deep Learning and Linguistic Representation
eBook - ePub

Deep Learning and Linguistic Representation

Shalom Lappin

  1. 168 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Deep Learning and Linguistic Representation

Shalom Lappin

Book details
Table of contents
Citations

About This Book

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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Table of contents

  1. Cover
  2. Half Title
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Dedication
  7. Contents
  8. Preface
  9. 1 Introduction: Deep Learning in Natural Language Processing
  10. 2 Learning Syntactic Structure with Deep Neural Networks
  11. 3 Machine Learning and the Sentence Acceptability Task
  12. 4 Predicting Human Acceptability Judgements in Context
  13. 5 Cognitively Viable Computational Models of Linguistic Knowledge
  14. 6 Conclusions and Future Work
  15. References
  16. ixAuthor Index
  17. xSubject Index