Word sense disambiguation is the
problem of finding the most likely senses for a sequence of words in a given
context. Disambiguation is a major step in most of the text applications.
However, the meanings of the words are highly dependent on the domain of the
text. Recently, word sense disambiguation is being addressed as an optimization
problem. For this, metaheuristics like simulated annealing and D-Bees are
developed.
In this paper, we try to answer the question about the compatibility
of general domain algorithms to solve specific domain word sense
disambiguation. For this, we propose two variants of the D-Bees algorithm to
include the domain information into the disambiguation process. The conceptsproposed in this paper are general and can be adapted to other algorithms. It
will be concluded that the D-Bees algorithm is suitable for solving specific
domain word sense disambiguation. It has a robust performance in general and
achieves competitive results compared with the simulated annealing method for different
datasets.
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