Word Sense Disambiguation Using an Evolutionary Approach
Abstract
Word sense disambiguation is a combinatorial problem consisting in the computational assignment of a
meaning to a word according to a particular context in which it occurs. Many natural language processing
applications, such as machine translation, information retrieval, and information extraction, require this
task which occurs at the semantic level. Evolutionary computation approaches can be effective to solve
this problem since they have been successfully used for many NP-hard optimization problems. In this
paper, we investigate main existing methods for the word sense disambiguation problem, propose a genetic
algorithm to solve it, and apply it to Modern Standard Arabic. We evaluated its performance on a large
corpus and compared it against those of some rival algorithms. The genetic algorithm exhibited more
precise prediction results.
meaning to a word according to a particular context in which it occurs. Many natural language processing
applications, such as machine translation, information retrieval, and information extraction, require this
task which occurs at the semantic level. Evolutionary computation approaches can be effective to solve
this problem since they have been successfully used for many NP-hard optimization problems. In this
paper, we investigate main existing methods for the word sense disambiguation problem, propose a genetic
algorithm to solve it, and apply it to Modern Standard Arabic. We evaluated its performance on a large
corpus and compared it against those of some rival algorithms. The genetic algorithm exhibited more
precise prediction results.
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