文献类型：会议论文

英文题名：Concept vector for semantic similarity and relatedness based on WordNet structure

作者：Liu, Hongzhe[1,2,3];Bao, Hong[1,2,3];Xu, De[1,2]

第一作者：Liu, Hongzhe;刘宏哲

通讯作者：Liu, HZ[1]

机构：[1]Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China;[2]Beijing Jiaotong Univ, Inst Comp Sci & Engn, Beijing, Peoples R China;[3]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China

第一机构：Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

会议论文集：23rd Brazilian Symposium on Software Engineering

会议日期：FEB, 2012

会议地点：Pernambuco, BRAZIL

语种：英文

外文关键词：Concept similarity; Concept relatedness; Concept vector model; Hierarchical concept tree; Hierarchical concept graph; WordNet

摘要：We define WordNet based hierarchy concept tree (HCT) and hierarchy concept graph (HCG), HCT contains hyponym/hypernym kind of relation in WordNet while HCG has more meronym/holonym kind of edges than in HCT, and present an advanced concept vector model for generalizing standard representations of concept similarity in terms of WordNet-based HCT. In this model, each concept node in the hierarchical tree has ancestor and descendent concept nodes composing its relevancy nodes, thus a concept node is represented as a concept vector according to its relevancy nodes' local density and the similarity of the two concepts is obtained by computing the cosine similarity of their vectors. In addition, the model is adjustable in terms of multiple descendent concept nodes. This paper also provides a method by which this concept vector may be applied with regard to HCG into HCT. With this model, semantic similarity and relatedness are computed based on HCT and HCG. The model contains structural information inherent to and hidden in the HCT and HCG. Our experiments showed that this model compares favorably to others and is flexible in that it can make comparisons between any two concepts in a WordNet-like structure without relying on any additional dictionary or corpus information. (C) 2011 Elsevier Inc. All rights reserved.