Ebook: Trajectories through Knowledge Space: A Dynamic Framework for Machine Comprehension

As any history student will tell you, all events must be understood within their political and sociological context. Yet science provides an interesting counterpoint to this idea, since scientific ideas stand on their own merit, and require no reference to the time and place of their conception beyond perhaps a simple citation. Even so, the historical context of a scientific discovery casts a special light on that discovery - a light that motivates the work and explains its significance against a backdrop of related ideas. The book that you hold in your hands is unusually adept at presenting technical ideas in the context of their time. On one level, Larry Bookman has produced a manuscript to satisfy the requirements of a PhD program. If that was all he did, my preface would praise the originality of his ideas and attempt to summarize their significance. But this book is much more than an accomplished disser­ tation about some aspect of natural language - it is also a skillfully crafted tour through a vast body of computational, linguistic, neurophysiological, and psychological research.

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Trajectories through Knowledge Space: A Dynamic Framework for MachineComprehension 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 MachineComprehension 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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Trajectories through Knowledge Space: A Dynamic Framework for MachineComprehension 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 MachineComprehension is an excellent reference for researchers and professionals, and may be used as an advanced text for courses on the topic.

Content:
Front Matter....Pages i-xxi
Introduction....Pages 1-21
An Overview of Connectionist and Probabilistic Approaches to Language Processing....Pages 23-48
Memory Architecture....Pages 49-64
The Basic Computation....Pages 65-97
Analysis of the Interpretation at the Relational and ASF Level....Pages 99-126
Reasoning from the Relational Level of the Representation....Pages 127-137
Experiments in Acquiring Knowledge from On-line Corpora....Pages 139-163
An Analysis of the Acquired Knowledge....Pages 165-190
Conclusions....Pages 191-204
Future Directions....Pages 205-209
Back Matter....Pages 211-271

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Trajectories through Knowledge Space: A Dynamic Framework for MachineComprehension 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 MachineComprehension is an excellent reference for researchers and professionals, and may be used as an advanced text for courses on the topic.

Content:
Front Matter....Pages i-xxi
Introduction....Pages 1-21
An Overview of Connectionist and Probabilistic Approaches to Language Processing....Pages 23-48
Memory Architecture....Pages 49-64
The Basic Computation....Pages 65-97
Analysis of the Interpretation at the Relational and ASF Level....Pages 99-126
Reasoning from the Relational Level of the Representation....Pages 127-137
Experiments in Acquiring Knowledge from On-line Corpora....Pages 139-163
An Analysis of the Acquired Knowledge....Pages 165-190
Conclusions....Pages 191-204
Future Directions....Pages 205-209
Back Matter....Pages 211-271
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