文献类型：期刊文献

英文题名：CONCEPT VECTOR FOR SIMILARITY MEASUREMENT BASED ON HIERARCHICAL DOMAIN STRUCTURE

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

第一作者：Liu, Hong-Zhe

通讯作者：Liu, HZ[1]

机构：[1]Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China;[2]Beijing Union Univ, Dept Comp Sci, Beijing, Peoples R China;[3]Beijing Jiaotong Univ, Inst Comp Sci & 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 Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China.

年份：2011

卷号：30

期号：5

起止页码：881-900

外文期刊名：COMPUTING AND INFORMATICS

收录：;EI(收录号：20131916316905);Scopus(收录号：2-s2.0-81255143551);WOS:【SCI-EXPANDED(收录号:WOS:000296412500001)】；

基金：This project was supported by the project supported by Beijing Municipal Commission of Education (Grant No. KM201111417002), Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (Grant No. PHR201108419 and Grant No. PHR 200907120), and the National Natural Science Foundation of China (Grant No. 6097-2045).

语种：英文

外文关键词：Concept similarity; concept vector model; cosine similarity measure; hierarchical taxonomy

摘要：The concept vector model generalizes standard representations of similarity concept in terms of tree-like structure. In the 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' density and the similarity of the two concepts is obtained by computing cosine similarity between their vectors. In addition, the model is adjusted in terms of local density and multiple descendents problem. The model contains structure information inherent and hidden in the tree. We show that this measure compares favorably to other measures, and it is flexible in that it can make comparisons between any two concepts in a hierarchical tree without relying on additional dictionary or corpus information.