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 hand-tuned 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-l measure basis, compared to the conventional similarity-based classification. On the other hand, the pattern-based features have appeared almost redundant.
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