Deep Learning and Linguistic Representation (Paperback, 1)

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 Index Subject Index