Paper: Refining the Notions of Depth and Density in WordNet-based Semantic Similarity Measures

ACL ID D11-1093
Title Refining the Notions of Depth and Density in WordNet-based Semantic Similarity Measures
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

We re-investigate the rationale for and the ef- fectiveness of adopting the notions of depth and density in WordNet-based semantic sim- ilarity measures. We show that the intuition for including these notions in WordNet-based similarity measures does not always stand up to empirical examination. In particular, the traditional definitions of depth and density as ordinal integer values in the hierarchical structure of WordNet does not always corre- late with human judgment of lexical semantic similarity, which imposes strong limitations on their contribution to an accurate similarity measure. We thus propose several novel defi- nitions of depth and density, which yield sig- nificant improvement in degree of correlation with similarity. When used in WordNet-based semantic similarity measures,...