Abstract
Genetic algorithms (GAs) have widely been investigated to solve hard optimization problems, including the word sense disambiguation (WSD). This problem asks to determine which sense of a polysemous word is used in a given context. The performance of a GA may drastically vary with the description of its genetic operators and selection methods, as well as the tuning of its parameters. In this paper, we present a self-adaptive GA for the WSD problem with an automated tuning of its crossover and mutation probabilities. The experimental results obtained on standard corpora (Senseval-2 (Task#1), SensEval- 3 (Task#1), SemEval-2007 (Task#7)) show that the proposed algorithm significantly outperformed a GA with standard genetic operators in terms of precision and recall.
Cite
CITATION STYLE
Alsaeedan, W., & El Menai, M. B. (2015). A self-adaptive genetic algorithm for the word sense disambiguation problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9101, pp. 581–590). Springer Verlag. https://doi.org/10.1007/978-3-319-19066-2_56
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