Memory-Based Language Processing

AvWalter Daelemans,Antal van den Bosch

Inbunden, Engelska, 2005

Del i serien Studies in Natural Language Processing

915 kr

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Häftad

566 kr

Beskrivning

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.

Produktinformation

Utgivningsdatum:2005-09-01
Mått:158 x 236 x 24 mm
Vikt:450 g
Format:Inbunden
Språk:Engelska
Serie:Studies in Natural Language Processing
Antal sidor:198
Förlag:Cambridge University Press
ISBN:9780521808903

Utforska kategorier

Språkvetenskap och lingvistik inom Språk och ordböcker

Mer om författaren

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.

Innehållsförteckning

1. Memory-based learning in natural language processing; 2. Inspirations from linguistics and artificial intelligence; 3. Memory and similarity; 4. Application to morpho-phonology; 5. Application to shallow parsing; 6. Abstraction and generalization; 7. Extensions.