Includes bibliographical references and index.

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.

"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"--