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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