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基于事件语义特征的中文文本蕴含识别 被引量：11

Event Semantic Feature Based Chinese Textual Entailment Recognition
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摘要 为了强化文本蕴含系统深层语义分析与推理能力,该文提出了基于事件语义特征的中文文本蕴含识别方法。该方法基于事件标注语料生成事件图,将文本间的蕴含关系转化为事件图间的蕴含关系;利用最大公共子图的事件图相似度算法计算事件语义特征,与统计特征、词汇语义特征和句法特征一起使用支持向量机进行分类,得到初步实验结果,再经过基于事件语义规则集合的修正处理得到最后的识别结果。实验结果表明基于事件语义特征的中文文本蕴含识别方法可以更有效地对中文文本蕴含关系进行识别。 In order to strengthen deep semantic analysis and inference of textual entailment,this paper proposes the method of event semantic feature based Chinese textual entailment recognition.The method generates event graphs base on event labeled corpus,and then the entailment recognition between text pairs can be changed to entailment recognition between event graphs.The event semantic feature can be computed based on max-common sub-graph.The event semantic feature combined with the surface statistical feature,lexical semantic feature and syntactic feature is used to classify textual entailment based on support vector machine and can obtain the preliminary experimental result,and the correction module based on event semantic rules handles preliminary experimental result to get the final experimental result.The experimental results show that the event semantic feature based Chinese textual entailment recognition is effective and efficient in Chinese textual entailment recognition.
作者 刘茂福 李妍 姬东鸿
机构地区 武汉科技大学计算机科学与技术学院 智能信息处理与实时工业系统湖北省重点实验室 武汉大学计算机学院
出处 《中文信息学报》 CSCD 北大核心 2013年第5期129-136,共8页 Journal of Chinese Information Processing
基金 国家自然科学基金资助项目(61100133 61173062) 国家社会科学基金重大资助项目(11&Z189) 湖北省教育厅人文社科基金重点资助项目(2011jyte126)
关键词 文本蕴含 事件语义特征 最大公共子图 支持向量机 Textual Entailment Event Semantic Feature Max-Common Sub-graph,Support Vector Machine
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献11

  • 1袁毓林,王明华.文本蕴涵的推理模型与识别模型[J].中文信息学报,2010,24(2):3-13. 被引量：16
  • 2Hideki Shima,Hiroshi Kanayama,Cheng-Wei Lee,et al.Overview of NTCIR-9 RITE:Recognizing Inference in TExt[C]//Proceedings of National Institute of Informatics.The 9th NTCIR Workshop Meeting on Evaluation of Information Access Technologies:Information Retrieval,Question Answering and Cross-Lingual Information Access.Tokyo,Japan:National Institute of Informatics,2011:291-301.
  • 3Yaoyun Zhang,Jun Xu,Chenlong Liu,et al.ICRC_HITSZ at RITE:Leveraging Multiple Classifiers Voting for Textual Entailment Recognition[C]//Proceedings of National Institute of Informatics.The 9th NT-CIR Workshop Meeting on Evaluation of Information Access Technologies:Information Retrieval,Question Answering and Cross-Lingual Information Access.Tokyo,Japan:National Institute of Informatics,2011:325-329.
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  • 6Ranxu Su,Sheng Shang,Pan Wang,et al.ZSWSL Text Entailment Recognizing System at NTCIR-9 RITE Task[C]//Proceedings of National Institute of Informatics.The 9th NTCIR Workshop Meeting on Evaluation of Information Access Technologies:Information Retrieval,Question Answering and Cross-Lingual Information Access.Tokyo,Japan:National Institute of Informatics,2011:394-399.
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  • 8Shih-Hung Wu,Wan-Chi Huang,Liang-Pu Chen,et al.Binary-class and Multi-class Chinese Textual Entailment System Description in NTCIR-9 RITE[C]//Proceedings of National Institute of Informatics.The 9th NTCIR Workshop Meeting on Evaluation of Information Access Technologies:Information Retrieval,Question Answering and Cross-Lingual Information Access.Tokyo,Japan:National Institute of Informatics,2011:422-426.
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二级参考文献19

  • 1Akhmatova, Elena. Textual Entailment Resolution via Atomic Proposition[C]//Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment. 2005.
  • 2Andreevskaia, Alina, Zhuoyan Li and Sabine Berger. Can Shallow Predicate Argument Structure Determine Entailment? [C]//Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment. 2005 :.
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共引文献15

同被引文献75

  • 1石晶,戴国忠.基于知网的文本推理[J].中文信息学报,2006,20(1):76-84. 被引量：8
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  • 9Berant J,Dagan I,Goldberger J.Global Learning of Typed Entailment Rules[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies.Stroudsburg,USA:Association for Computational Linguistics,2011:610-619.
  • 10Melamud O,Berant J,Dagan I,et al.A Two Level Model for Context Sensitive Inference Rules[C]//Proceedings of the 19th International Conference on Technologies and Applications of Artificial Intelligence.Berlin,Germany:Springer,2014:310-321.

引证文献11

二级引证文献89

中文信息学报
2013年 第5期

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