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Deep Learning and Linguistic Representation
OUTLINE OF THE BOOK
FROM ENGINEERING TO COGNITIVE SCIENCE
ELEMENTS OF DEEP LEARNING
TYPES OF DEEP NEURAL NETWORKS
AN EXAMPLE APPLICATION
SUMMARY AND CONCLUSIONS
: Learning Syntactic Structure with Deep Neural Networks
SUBJECT-VERB AGREEMENT
ARCHITECTURE AND EXPERIMENTS
HIERARCHICAL STRUCTURE
TREE DNNS
SUMMARY AND CONCLUSIONS
: Machine Learning and the Sentence Acceptability Task
GRADIENCE IN SENTENCE ACCEPTABILITY
PREDICTING ACCEPTABILITY WITH MACHINE LEARNING MODELS
ADDING TAGS AND TREES
SUMMARY AND CONCLUSIONS
: Predicting Human Acceptability Judgements in Context
ACCEPTABILITY JUDGEMENTS IN CONTEXT
TWO SETS OF EXPERIMENTS
THE COMPRESSION EFFECT AND DISCOURSE COHERENCE
PREDICTING ACCEPTABILITY WITH DIFFERENT DNN MODELS
SUMMARY AND CONCLUSIONS
: Cognitively Viable Computational Models of Linguistic Knowledge
HOW USEFUL ARE LINGUISTIC THEORIES FOR NLP APPLICATIONS?
MACHINE LEARNING MODELS VS FORMAL GRAMMAR
EXPLAINING LANGUAGE ACQUISITION
DEEP LEARNING AND DISTRIBUTIONAL SEMANTICS
SUMMARY AND CONCLUSIONS
REPRESENTING SYNTACTIC AND SEMANTIC KNOWLEDGE
DOMAIN-SPECIFIC LEARNING BIASES AND LANGUAGE ACQUISITION
DIRECTIONS FOR FUTURE WORK
References
Author Index
Subject Index
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