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

We investigate the usefulness of evolutionary al gorithms in three incarnations of the problem of feature relevance assignment in memory based language processing MBLP feature weight ing feature ordering and feature selection We use a simple genetic algorithm ga for this problem on two typical tasks in natural lan guage processing morphological synthesis and unknown word tagging We nd that ga fea ture selection always signi cantly outperforms the MBLP variant without selection and that feature ordering and weighting with ga signi cantly outperforms a situation where no weight ing is used However ga selection does not sig ni cantly do better than simple iterative feature selection methods and ga weighting and order ing reach only similar performance as current information theoretic feature weighting meth ods Memory Based Language