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

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

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Masato Hagiwara
• Keywords: ACL 2008, Computational Linguistics, Distributional Similarity, Pattern-based Features, Similarity Measures