Hyponymy Graph Model for Word Semantic Similarity Measurement

作     者：WANG Junhua ZUO Wanli PENG Tao 

作者机构：College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering attached to the Ministry of Education 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2015年第24卷第1期

页      面：96-101页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(No.60973040,No.61300148,No.60903098) the Key Scientific and Technological Break-through Program of Jilin Province(No.20130206051GX) 

主　　题：Word semantic similarity Concept semantic similarity Semantic similarity Hyponymy graph model Word Net. 

摘      要：Measuring word semantic similarity is a generic problem with a broad range of applications such as ontology mapping, computational linguistics and artificial intelligence. Previous approaches to computing word semantic similarity did not consider concept occurrence frequency and word’s sense number. This paper introduced Hyponymy graph, and based on which proposed a novel word semantic similarity model. For two words to be compared, we first retrieve their related concepts; then produce lowest common ancestor matrix and distance matrix between concepts; finally calculate distance-based similarity and information-based similarity, which are integrated to get final semantic similarity. The main contribution of our method is that both concept occurrence frequency and word’s sense number are taken into account. This similarity measurement more closely fits with human rating and effectively simulates human thinking process. Our experimental results on benchmark dataset M&C and R&G with Word Net2.1 as platform demonstrate roughly 0.9%–1.2%improvements over existing best approaches.