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山东大学学报（理学版） ›› 2015, Vol. 50 ›› Issue (11): 74-80.doi: 10.6040/j.issn.1671-9352.3.2014.095

• 论文 • 上一篇    下一篇

基于浅层语义分析的主题事件的时间识别

李风环, 郑德权, 赵铁军   

  1. 哈尔滨工业大学计算机科学与技术学院, 黑龙江 哈尔滨 150001
  • 收稿日期:2015-03-03 修回日期:2015-04-21 出版日期:2015-11-20 发布日期:2015-12-09
  • 通讯作者: 郑德权(1968-),男,博士,副教授,研究方向为信息抽取、知识工程、自然语言处理.E-mail:dqzheng2007@gmail.com E-mail:dqzheng2007@gmail.com
  • 作者简介:李风环(1985-),女,博士,研究方向为事件抽取、时序分析、数据挖掘、自然语言处理.E-mail:finelee2012@gmail.com
  • 基金资助:
    国家自然科学基金资助项目(61402134);国家国际科技合作专项(2014DFA11350)

Temporal recognition for topic event based on shallow semantic parsing

LI Feng-huan, ZHENG De-quan, ZHAO Tie-jun   

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
  • Received:2015-03-03 Revised:2015-04-21 Online:2015-11-20 Published:2015-12-09

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摘要： 时间识别是自然语言处理中极其重要的课题。事件中与主题相关的时间信息体现了事件在时间维度的主题特征。当前面向事件的时间识别大多是基于句子或短语的,并采用静态时间值机制。本文提出了一个面向主题事件的时间识别模型。该模型采用参考时间动态选择机制对时间表达式规范化。结合事件抽取和浅层语义分析,将浅层语义分析结果和时间表达式进行映射,将基于句子或短语的时间识别转化为基于篇章的时间识别,从而识别主题事件片段的时间。实验表明所提出的方法使主题事件片段的时间识别的性能提高了9.6%。

关键词: 时间识别, 事件抽取, 浅层语义分析, 主题事件, 动态

Abstract: Temporal recognition is a key subject in natural language processing community. The topic-related temporal information reflects the topic feature of topic events on temporal dimensionality. Most temporal recognition for events was sentence-oriented or phrase-oriented and employed static time-value machine. A temporal recogtion model for topic events was proposed in this paper. Temporal expressions were normalized with reference time dynamic-choosing mechanism in this model. Combining event extraction and shallow semantic parsing, semantic roles were mapped to temporal expressions. Document-oriented temporal recognition was implemented using sentence-oriented or phrase-oriented temporal recognition, consequently, temporal recognition for topic event segments was realized. Results show that performances of temporal recognition for topic event segments are improved by 9.6%.

Key words: topic event, event extraction, shallow semantic parsing, dynamic, temporal recognition

中图分类号: 

  • TP391
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