A Semantic Similarity Measure between Nouns based on the Structure of Wordnet

Abstract

Several approaches for computing semantic similarity and relatedness measures between terms have been developed. This paper proposes a new semantic similarity measure between two nodes concentrating on nouns as well as their hypernym/hyponym relationships based on the structure of Wordnet. In particular, the similarity of two given nouns depends not only on their positions but also on their relevancy connections in the hierarchy. We evaluate our measure and the other ones on dataset of Miller-Charles and then compute the correlation coefficients to the human judgments. The experimental results show that our method outperforms edge-counting methods.