Negation and uncertainty information extraction oriented to natural language text

被引：0

作者：

Zou B.-W. ^{[1
]}

Qian Z. ^{[1
]}

Chen Z.-C. ^{[1
]}

Zhu Q.-M. ^{[1
]}

Zhou G.-D. ^{[1
]}

机构：

[1] School of Computer Science and Technology, Soochow University, Suzhou

来源：

Ruan Jian Xue Bao/Journal of Software | 2016年 / 27卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Cue detection; Information extraction; Negation information; Scope resolution; Uncertainty information;

D O I：

10.13328/j.cnki.jos.004860

中图分类号：

学科分类号：

摘要：

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. © Copyright 2016, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

引用

页码：309 / 328

页数：19

共 34 条

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