A new hybrid improved method for measuring concept semantic similarity in WordNet

Abstract EN

Abstract: Computing semantic similarity between concepts is an important issue in natural language processing, artificial intelligence, information retrieval and knowledge management.

The measure of computing concept similarity is a fundament of semantic computation.

In this paper, we analyze typical semantic similarity measures and note Wu and Palmer’s measure which does not distinguish the similarities between nodes from a node to different nodes of the same level.

Then, we synthesize the advantages of measure of path-based and IC-based, and propose a new hybrid method for measuring semantic similarity.

By testing on a fragment of WordNet hierarchical tree, the results demonstrate the proposed method accurately distinguishes the similarities between nodes from a node to different nodes of the same level and overcome the shortcoming of the Wu and Palmer’s measure.