Negation and Uncertainty Information Extraction Oriented to Natural Language Text
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National Natural Science Foundation of China (61272260, 61331011, 61273320)

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    Abstract:

    The current research on information extraction mainly focuses on affirmative information. However there are more negation and uncertainty information in natural language texts. For purpose of separating them from affirmative information, it is necessary to make an intensive study of negation and uncertainty information extraction. For this task, this study firstly constructs a Chinese corpus including 16 841 sentences. Employing the sequence labeling model and the convolution tree kernel model, it systematically explores the efficiency of various kinds of serialized dependency features and structured parsing features. Finally, it proposes a meta-decision tree model to integrate the above two models. Experimental results show that the performances of the new method on negation and uncertainty information extraction achieve 69.84% and 58.57% of accuracy respectively, providing a solid foundation for related studies in the future.

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邹博伟,钱忠,陈站成,朱巧明,周国栋.面向自然语言文本的否定性与不确定性信息抽取.软件学报,2016,27(2):309-328

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History
  • Received:January 30,2015
  • Revised:April 14,2015
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  • Online: November 12,2015
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