MINING LEXICAL HYPONYMY RELATIONS FROM LARGE-SCALE CONCEPT SET

摘要：

Inner structures of Chinese lexical concepts have embedded some useful semantic relations. In this paper, we proposed a new statistical approach to mine lexical hyponymy relations from large-scale concept set, instead of analyzing inner structures. Firstly we designed Common Suffix Tree to cluster the lexical concept set. Class Concepts are then extracted by statistic-base rules we investigated in concept set Finally, we export hyponymy relations from the common suffix tree. Experimental result showed us that this approach achieved a precision of 95.833％ and a recall of 67.241％ when the concept size achieved 800,000.

关键词： Knowledge Acquisition Lezical Hyponymy Relation Acquisition Information Eztraction Common Suffiz Tree Suffiz Probability Inflezion Rule

作者: JIA-YU ZHOU YAN PU JING-JING LI

作者单位: School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 281-286

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）