Memory-Based Language Processing

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Cambridge University Press, Sep 1, 2005 - Language Arts & Disciplines
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Memory-based language processing - a machine learning and problem solving method for language technology - is based on the idea that the direct reuse of examples using analogical reasoning is more suited for solving language processing problems than the application of rules extracted from those examples. This book discusses the theory and practice of memory-based language processing, showing its comparative strengths over alternative methods of language modelling. Language is complex, with few generalizations, many sub-regularities and exceptions, and the advantage of memory-based language processing is that it does not abstract away from this valuable low-frequency information. By applying the model to a range of benchmark problems, the authors show that for linguistic areas ranging from phonology to semantics, it produces excellent results. They also describe TiMBL, a software package for memory-based language processing. The first comprehensive overview of the approach, this book will be invaluable for computational linguists, psycholinguists and language engineers.
 

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Contents

Chapter 1 MemoryBased Learning in Natural Language Processing
3
Chapter 2 Inspirations from linguistics and artificial intelligence
15
Chapter 3 Memory and Similarity
26
Chapter 4 Application to morphophonology
57
Chapter 5 Application to shallow parsing
85
Chapter 6 Abstraction and generalization
104
Chapter 7 Extensions
148
Bibliography
168
Index
186
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About the author (2005)

Walter Daelemans is Professor of Computational Linguistics and AI in the Department of Linguistics, University of Antwerp.

Antal van den Bosch is Assistant Professor in the Department of Computational Linguistics and AI, Tilburg University.

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