Trajectories through Knowledge Space: A Dynamic Framework for
Machine Comprehension provides an overview of many of the main
ideas of connectionism (neural networks) and probabilistic natural
language processing. Several areas of common overlap between these
fields are described in which each community can benefit from the
ideas and techniques of the other. The author's perspective on
comprehension pulls together the most significant research of the
last ten years and illustrates how we can move more forward onto
the next level of intelligent text processing systems. A central
focus of the book is the development of a framework for
comprehension connecting research themes from cognitive psychology,
cognitive science, corpus linguistics and artificial intelligence.
The book proposes a new architecture for semantic memory, providing
a framework for addressing the problem of how to represent
background knowledge in a machine. This architectural framework
supports a computational model of comprehension.Trajectories
through Knowledge Space: A Dynamic Framework for Machine
Comprehension is an excellent reference for researchers and
professionals, and may be used as an advanced text for courses on
the topic.
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