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Genetic Algorithms for Feature Relevance Assignment in Memory-Based Language Processing

We investigate the usefulness of evolutionary algorithms in three incarnations of the problem of feature relevance assignment in memory-based language processing (MBLP): feature weighting, feature ordering and feature selection. We use a simple genetic algorithm (GA) for this problem on two typical tasks in natural language processing: morphological synthesis and unknown word tagging. We find that GA feature selection always significantly outperforms the MBLP variant without selection and that feature ordering and weighting with GA significantly outperforms a situation where no weighting is used. However, GA selection does not significantly do better than simple iterative feature selection methods, and GA weighting and ordering reach only similar performance as current information-theoretic feature weighting methods.


Anne Kool, Walter Daelemans and Jakub Zavrel, Genetic Algorithms for Feature Relevance Assignment in Memory-Based Language Processing. In: Proceedings of CoNLL-2000 and LLL-2000, Lisbon, Portugal, 2000. [ps] [pdf] [bibtex]
Last update: June 27, 2001. erikt@uia.ua.ac.be