In natural language processing, the problem of finding the intended meaning or “sense” of a word which is activated by the use of that word in a particular context is generally known as word sense disambiguation (WSD) problem. The solution to this problem impacts many other fields of natural language processing including sentiment analysis and machine translation. Here, WSD problem is modelled as a combinatorial optimization problem where the goal is to find a sequence of meanings or senses that maximizes the semantic meaning among the targeted words. In this work, an algorithm is proposed that uses a combinatorial version of particle swarm optimization algorithm for solving WSD problem. The test results show that the algorithm performs better than existing methods.
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