In recent years, great advances have been made in the speed,
accuracy, and coverage of automatic word sense disambiguators -
systems that given a word appearing in a certain context, can
identify the sense of that word. Word Sense Disambiguation (WSD) is
traditionally considered an AI-hard problem, that is, a problem
which can be solved only by first resolving all the difficult
problems in artificial intelligence (AI), such as the
representation of common sense and encyclopedic knowledge.
Moreover, it has been found that people are inconsistent when asked
to disambiguate words and this causes problems when testing the
output of an automatic disambiguator. A breakthrough in this field
would have a significant impact on many relevant web-base
applications, such as information retrieval and information
extraction. In the review of disambiguation research, many varied
techniques for performing automatic disambiguation are introduced.
In this book, three different language independent strategies for
word sense disambiguation are proposed and evaluated. The
performances of the resulting system have been compared with
respect to other well know methods for word sense disambiguation.
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