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计算机工程 ›› 2011, Vol. 37 ›› Issue (17): 143-145. doi: 10.3969/j.issn.1000-3428.2011.17.048

• 人工智能及识别技术 • 上一篇    下一篇

基于语义角色的实体关系抽取

毛小丽,何中市,邢欣来,刘 莉   

  1. (重庆大学计算机学院,重庆 400044)
  • 收稿日期:2011-03-08 出版日期:2011-09-05 发布日期:2011-09-05
  • 作者简介:毛小丽(1986-),女,硕士研究生,主研方向:自然语言处理;何中市,教授、博士生导师;邢欣来,博士研究生;刘 莉,硕士研究生
  • 基金资助:
    国家科技重大专项基金资助项目(2008ZX07315-001); 中央高校基本科研业务费专项基金资助项目(CDJXS11180020)

Entity Relation Extraction Based on Semantic Role

MAO Xiao-li, HE Zhong-shi, XING Xin-lai, LIU Li   

  1. (College of Computer Science, Chongqing University, Chongqing 400044, China)
  • Received:2011-03-08 Online:2011-09-05 Published:2011-09-05

摘要: 提出一种实体关系抽取方案,该方案根据实体关系抽取的特点,在常用特征基础上新增语义角色特征用于构建特征向量,并利用支持向量机构造分类器。在SemEval-2010评测任务8提供的数据上进行实验,在判断候选实体对的关系类型上F1值达到81.6%,与未加入语义角色特征相比提高4%,结果表明该方案语义角色特征有利于实体语义关系抽取。

关键词: 关系抽取, 语义角色特征, 支持向量机, 特征向量, 分类器

Abstract: This paper proposes a new entity relation extraction method with a variety of common features plus semantic role features according to the characteristics of entity relation extraction and using the feature vector to build a SVM-classifier. When judging the relation type, this method achieves the F1 measure of 81.6% which is increased by 4% compared with no semantic role features on the data supplied by the evaluation task eight of SemEval-2010. Experimental results show that semantic role feature is benefit to entity relation extraction.

Key words: relation extraction, semantic role features, Support Vector Machine(SVM), feature vector, classifier

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