Trajectories Through Knowledge Space : A Dynamic Framework for Machine Comprehension

A Dynamic Framework for Machine Comprehension

By: Lawrence A. Bookman

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Hardcover | 1 December 2009

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Hardcover
296 Pages

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25.4 x 17.15 x 2.54

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"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 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" should be a useful reference for researchers and professionals, and may be used as an advanced text for courses on the topic.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Natural Language & Machine Translation Language & Linguistics Linguistics Audio Processing

List Of Figures
List Of Tables
Foreword
Preface
Introduction	p. 1
An Overview of Connectionist and Probabilistic Approaches to Language Processing	p. 23
Memory Architecture	p. 49
The Basic Computation	p. 65
Analysis of the Interpretation at the Relational and ASF Level	p. 99
Reasoning from the Relational Level of the Representation	p. 127
Experiments in Acquiring Knowledge from On-line Corpora	p. 139
An Analysis of the Acquired Knowledge	p. 165
Conclusions	p. 191
Future Directions	p. 205
App. A: The ASFs Used in the LeMICON Experiments	p. 211
App. B: A Formal Analysis of the Dynamics	p. 217
App. C: Sample Parsed Input to LeMICON	p. 227
App. D: Additional Results with SSS	p. 229
App. E: Proof of the Boundedness of the Measure [actual symbol not reproducible]	p. 233
App. F: The Dictionary Trees that Describe the Class "Space"	p. 235
References	p. 237
Author Index	p. 259
Subject Index	p. 265
Table of Contents provided by Blackwell. All Rights Reserved.

Trajectories Through Knowledge Space : A Dynamic Framework for Machine Comprehension

A Dynamic Framework for Machine Comprehension

At a Glance

Hardcover

Shipping

How to return your order

Defective items

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