A New Hybrid Improved Method for Measuring
Concept Semantic Similarity in WordNet
Xiaogang Zhang, Shouqian Sun, and Kejun
Zhang
College of Computer Science and Technology, Zhejiang University,
Hangzhou, China
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.
Keywords: Information content, Semantic similarity, WordNet taxonomy, Hyponym.
Received May 25, 2017; accepted April 25, 2018