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.