Paper: Genetic Algorithms For Feature Relevance Assignment In Memory-Based Language Processing

ACL ID W00-0720
Title Genetic Algorithms For Feature Relevance Assignment In Memory-Based Language Processing
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2000
Authors

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 find that GA fea- ture selection always significantly outperforms the MBLP variant without selection and that feature ordering and weighting with CA signifi- cantly outperforms a situation where no weight- ing is used. However, GA selection does not sig- nificantly do better than simple iterative feature selection methods, and GA weighting and order- ing reach only similar performance as current info...