摘要
传统的信息抽取方法由于缺少语义信息的支持,抽取的准确率不高。针对这个问题提出了一种基于语义理解的信息抽取方法。一方面,把语义角色标注的浅层语义信息转换成概念图,无歧义地将抽取信息所包含的基本语义形式化;另一方面,通过概念图的相似度计算区分场景,并使用语义角色获取抽取模式,以提高抽取质量。实验结果表明,该方法取得了较好的效果。
Because the traditional information extraction approaches are lack of semantic information, the accuracy is not high in extraction. In order to solve the problem, this article proposed a novel method of information extraction based on semantic role and concept graph. On one hand, the process transformed the shallow semantic information into concept graphs, and accurately described the main meaning of sentences. On the other band, the calculator computed the similarity of concept graphs so as to distinguish the different domains of information. Meanwhile, the mapping rules would be got by using semantic role for improving the quality of extraction. The experimental results show that this method of information extraction is feasible and effective.
出处
《计算机应用》
CSCD
北大核心
2010年第2期411-414,共4页
journal of Computer Applications
关键词
信息抽取
语义角色
概念图相似度
知网
文本理解
information extraction
semantic role
similarity of concept graphs
Hownet
text understanding
