结合对数似然比的领域本体概念和关系的提取  

Domain ontology concept and relation extraction using log-likelihood ratio

作　　者：张玉芳[1] 舒万里[1] 熊忠阳[1] 

机构地区：[1]重庆大学计算机学院,重庆400044

出　　处：《计算机工程与应用》2013年第6期148-151,共4页Computer Engineering and Applications

基　　金：中央高校研究生科技创新基金(No.CDJXS11180013)

摘　　要：本体学习已成为计算机领域的一个研究热点,目前本体学习的研究重点在于概念及关系的提取。针对现有学习方法准确率不高,提出一种结合对数似然比(Log-Likelihood Ratio,LLR)的本体学习方法,采用对数似然比计算概念与领域及概念与概念之间的相关性,将其应用到概念与关系提取中。实验结果表明,结合对数似然比的学习方法能够有效改进概念和关系提取的准确度。Ontology learning has become a hot research in the field of computer. Ontology learning currently focuses on concept extraction and conceptual relation extraction. Concerning the poor accuracy of existing learning methods, a new learning method based on Log-Likelihood Ratio （LLR） is proposed. In this way, the concepts and concept pairs can be extracted from Chinese text for estimating the significance of concepts and conceptual relations. The experimental results show that the method can effec- tively improve the performance of concept extraction and relation extraction.

关 键 词：本体学习 概念提取 关系提取 对数似然比 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]