"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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List of Figures. List of Tables. Foreword. Preface. 1. Introduction. 2. An Overview of Connectionist and Probabilistic Approaches to Language Processing. 3. Memory Architecture. 4. The Basic Computation. 5. Analysis of the Interpretation at the Relational and ASF Level. 6. Reasoning from the Relational Level of the Representation. 7. Experiments in Acquiring Knowledge from On-Line Corpora. 8. An Analysis of the Acquired Knowledge. 9. Conclusions. 10. Future Directions. Appendices: A. The ASFs Used in the LeMICON Experiments. B. A Formal Analysis of the Dynamics. C. Sample Parsed Input to LeMICON. D. Additional Results with SSS. E. Proof of the Boundedness of the Measure I. F. The Dictionary Trees that Describe the Class `Space'. References. Author Index. Subject Index.
Book by Bookman Lawrence A
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Condizione: New. Provides an overview of many of the main ideas of connectionism (neural networks) and probabilistic natural language processing. This title describes several areas of common overlap between these fields in which each community can benefit from the ideas and techniques of the other. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 271 pages, biography. BIC Classification: UYQL. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 17. Weight in Grams: 1310. . 1994. Hardback. . . . . Codice articolo V9780792394877
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