Paper: A Supervised Learning Approach to Automatic Synonym Identification Based on Distributional Features

ACL ID P08-3001
Title A Supervised Learning Approach to Automatic Synonym Identification Based on Distributional Features
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

Distributional similarity has been widely used to capture the semantic relatedness of words in many NLP tasks. However, various parame- ters such as similarity measures must be hand- tuned to make it work effectively. Instead, we propose a novel approach to synonym iden- tification based on supervised learning and distributional features, which correspond to the commonality of individual context types shared by word pairs. Considering the inte- gration with pattern-based features, we have built and compared five synonym classifiers. The evaluation experiment has shown a dra- matic performance increase of over 120% on the F-1 measure basis, compared to the con- ventional similarity-based classification. On the other hand, the pattern-based features have appeared almost redundant.