Refining the Notions of Depth and Density in WordNet-based Semantic Similarity Measures
Tong Wang, Graeme Hirst · 2011
We re-investigate the rationale for and the effectiveness of adopting the notions of depth and density in WordNet-based semantic similarity 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 correlate with human judgment of lexical semantic similarity, which imposes strong limitations on their contribution to an accurate similarity measure. We thus propose several novel definitions of depth and density, which yield significant improvement in degree of correlation with similarity. When used in WordNet-based semantic similarity measures, the new definitions consistently improve performance on a task of correlating with human judgment. Another important resource in the latter stream is semantic taxonomies such as WordNet (Fellbaum, 1998). Despite their high cost of compilation and limited availability across languages, semantic taxonomies have been widely used in similarity measures, and one of the main reasons behind this is that the often complex notion of lexical semantic similarity can be approximated with ease by the distance between words (represented as nodes) in their hierarchical structures, and this approximation appeals much to our intuition. Even methods as simple as “hop counts ” between nodes (e.g., that of Rada et al. 1989 on the English WordNet) can take us a long way. Meanwhile, taxonomy-based methods have been constantly refined by incorporating various structural features such as depth (Sussna, 1993; Wu and Palmer, 1994), density (Sussna, 1993), type of connection (Hirst and St-Onge, 1998; Sussna, 1993), word class (sense) frequency estimates (Resnik, 1999), or a combination these features (Jiang and Conrath, 1997). Most of these algorithms are fairly 1