Memory-based language processing - a machine learning and problem solving
method for language technology - is based on the idea that the direct
re-use 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.