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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