Words have different
meanings based on the context of the word usage in a sentence. Word sense is
one of the meanings of a word. Human language is ambiguous, so that many words
can be interpreted in multiple ways depending on the context in which they
occur. Word sense disambiguation (WSD) is the ability to identify the meaning
of words in context in a computational manner. WSD is considered an AIcomplete
problem, that is, a task whose solution is at least as hard as the most
difficult problems in artificial intelligence.
WSD can be viewed as
a classification task: word senses are the classes, and an automatic
classification method is used to assign each occurrence of a word to one or
more classes based on the evidence from the context and from external knowledge
sources. WSD heavily relies on knowledge. Knowledge sources provide data which
are essential to associate senses with words.
The assessment of WSD
systems is discussed in the context of the Senseval/Semeval campaigns, aiming
at the objective evaluation of systems participating in several different
disambiguation tasks. Here, some of the knowledge sources used in WSD,
different approaches for WSD (supervised, unsupervised and Knowledge-based )
and evaluation of WSD systems are discussed. The applications of WSD are also
seen.
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