Paper: Transfer Learning Feature Selection and Word Sense Disambiguation

ACL ID P09-2065
Title Transfer Learning Feature Selection and Word Sense Disambiguation
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2009
Authors

We propose a novel approach for improv- ing Feature Selection for Word Sense Dis- ambiguation by incorporating a feature relevance prior for each word indicating which features are more likely to be se- lected. We use transfer of knowledge from similar words to learn this prior over the features, which permits us to learn higher accuracy models, particularly for the rarer word senses. Results on the ONTONOTES verb data show significant improvement over the baseline feature selection algo- rithm and results that are comparable to or better than other state-of-the-art methods.