计算机科学 ›› 2013, Vol. 40 ›› Issue (10): 221-225.

• 人工智能 • 上一篇    下一篇

基于故事浅层理解与事件框架的语义建模

谢秋妹,高春鸣,王小兰   

  1. 湖南大学信息科学与工程学院 长沙410082 湖南大学数字媒体研究所 长沙410082;湖南大学信息科学与工程学院 长沙410082 湖南大学数字媒体研究所 长沙410082;湖南大学信息科学与工程学院 长沙410082 湖南大学数字媒体研究所 长沙410082
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受广东省教育部产学研结合项目(2011B090400002)资助

Semantic Modeling for Story Based Shallow Text Understanding and Event Frame

XIE Qiu-mei,GAO Chun-ming and WANG Xiao-lan   

  • Online:2018-11-16 Published:2018-11-16

摘要: 针对故事文本的语义理解需要,采用开放式信息抽取方式对故事文本进行多元事实抽取,并将多元事实框架表示成事件语义模型。本方法提出了基于依存关系分析和正则表达式相结合的多元事实抽取方法,得到故事浅层语义的多元事实框架,然后将多元事实框架通过规则映射到具有定量时空描述的事件本体模型即Story-Oriented Semantic Description Language(SOSDL)本体。实验表明,多元事实抽取方法能抽取出较多的事实,具有较高的准确率,且SOSDL本体能有效地表示多元事实框架的事件、语义要素以及它们之间的关系。

关键词: 开放式信息抽取,自然语言处理,故事文本,事件本体

Abstract: For the semantic understanding task of story text,this paper used open information extraction method to capture N-ary facts from story,and then described N-ary facts frames as event semantic model.Our method proposes extraction rules for frame elements based on dependency parser and regular expressions,and an event semantic model SOSDL ontology for story text with representation of qualitative temporal and spatial relations.Our experiments indicate that this approach captures more facts per sentence,is greater completeness, and SOSDL can effectively model the semantic elements of N-ary facts frames and their relationship.

Key words: Open information extraction,Natural language processing,Story text,Event ontology

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