A Hierarchical Semantics-Aware Distributional Similarity Scheme^∗

The context type and similarity calculation are two essential features of a distributional similarity scheme (DSS). In this paper, we propose a hierarchical semantic-aware DSS that exploits semantic relation words as extra context information to guide the similarity calculation. First, we define and extract five types of semantic relations, and then develop relation-based similarities from the distributional similarities among the top-ranked relation words. Finally, we integrate various similarities using learning-to-rank technique. Experiments show that semantic relations are beneficial to predicting accurate similarity. On 6904 pairwise similarity comparisons, the predictive accuracy of our approach reaches 83.9%, which significantly outperforms the baseline approaches. We also conduct intrinsic analysis by varying the quality of semantic relations and the usage of individual similarities.