Memory-Based Language Processing

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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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Chapter 1 MemoryBased Learning in Natural Language Processing
Chapter 2 Inspirations from linguistics and artificial intelligence
Chapter 3 Memory and Similarity
Chapter 4 Application to morphophonology
Chapter 5 Application to shallow parsing
Chapter 6 Abstraction and generalization
Chapter 7 Extensions

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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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