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计算机工程

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基于深层句法分析的生物事件触发词抽取

王 健,吴 雨,林鸿飞,杨志豪   

  1. (大连理工大学计算机科学与技术学院,辽宁 大连 116023)
  • 收稿日期:2013-08-19 出版日期:2014-01-15 发布日期:2014-01-13
  • 作者简介:王 健(1967-),女,副教授,主研方向:文本挖掘,机器学习,信息抽取;吴 雨,硕士研究生;林鸿飞,教授、博士、博士生导师;杨志豪,副教授、博士、博士生导师
  • 基金资助:

    国家自然科学基金资助项目(61272373, 61070098);辽宁省自然科学基金资助项目(201202031)

Biological Event Trigger Word Extraction Based on Deep Syntactic Parsing

WANG Jian, WU Yu, LIN Hong-fei, YANG Zhi-hao   

  1. (School of Computer Science and Technology, Dalian University of Technology, Dalian 116023, China)
  • Received:2013-08-19 Online:2014-01-15 Published:2014-01-13

摘要:

传统利用语义和句法信息进行生物事件抽取的方法,在触发词抽取阶段句法信息运用形式单一笼统,不能有效发挥作用。为此,提出一种基于深层句法分析的触发词抽取方法。该方法采用间接的句法信息模式,利用深层句法信息独立地进行边检测,将边检测结果融合于触发词抽取中,使深层句法信息得到更有效的利用。在BioNLP 2009与2011共享任务语料上进行实验,结果表明,该方法的F值达到68.8%和67.3%,具有较好的触发词抽取性能。

关键词: 生物事件, 触发词, 谓词参数结构, 深层句法分析, 边检测, 事件元素

Abstract:

Due to the simplistic and shallow application mode, syntactic information can not effectively play a role in the trigger recognition phase of traditional biological event extraction methods based on semantic and syntactic information. This paper describes a trigger extraction method based on the deep syntactic analysis. To make more effective utilization of the deep syntactic information, a unique indirect application mode is adopted. Deep syntactic information is used for edge detection, and the result is merged into the trigger extraction phase. Experimental results on BioNLP 2009 and 2011 shared tasks data achieve F-scores of 68.8% and 67.3%, which shows that the method has a good performance on biomedical event trigger extraction.

Key words: biological event, trigger word, predicate parameter structure, deep syntactic parsing, edge detection, event element

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