[1]王立凯,曲维光,魏庭新,等.基于深度学习的中文零代词识别[J].南京师范大学学报(工程技术版),2021,21(04):019-26.[doi:10.3969/j.issn.1672-1292.2021.04.004]
　Wang Likai,Qu Weiguang,Wei Tingxin,et al.Identification of Chinese Zero Pronouns Based on Deep Learning[J].Journal of Nanjing Normal University(Engineering and Technology),2021,21(04):019-26.[doi:10.3969/j.issn.1672-1292.2021.04.004]
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基于深度学习的中文零代词识别
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
21卷
期数:
2021年04期
页码:
019-26
栏目:
计算机科学与技术
出版日期:
2021-12-15

文章信息/Info

Title:
Identification of Chinese Zero Pronouns Based on Deep Learning
文章编号:
1672-1292(2021)04-0019-08
作者:
王立凯1; 曲维光1; 2; 魏庭新3; 周俊生1; 顾彦慧1; 李 斌2
(1.南京师范大学计算机与电子信息学院,江苏 南京 210023)(2.南京师范大学文学院,江苏 南京 210097)(3.南京师范大学国际文化教育学院,江苏 南京 210097)
Author(s):
Wang Likai1; Qu Weiguang1; 2; Wei Tingxin3; Zhou Junsheng1; Gu Yanhui1; Li Bin2
(1.School of Computer and Electronic Information,Nanjing Normal University,Nanjing 210023,China)(2.School of Chinese Language and Literature,Nanjing Normal University,Nanjing 210097,China)(3.International College for Chinese Studies,Nanjing Normal University,Nanjing 210097,China)
关键词:
深度学习; 中文零指代; 零代词识别; Tree-LSTM; 注意力机制
Keywords:
deep learning; Chinese zero pronoun; zero pronoun identification; Tree-LSTM; attention
分类号:
TP391
DOI:
10.3969/j.issn.1672-1292.2021.04.004
文献标志码:
A
摘要:
针对中文零代词识别任务,提出了一种基于深度神经网络的中文零代词识别模型. 首先,通过注意力机制利用零代词的上下文来帮助表示缺省的语义信息. 然后,利用Tree-LSTM挖掘零代词上下文的句法结构信息. 最后,利用语义信息和句法结构信息的融合特征识别零代词. 实验结果表明,相对于以往的零代词识别方法,该方法能够有效提升识别效果,在中文OntoNotes5.0数据集上的F1值达到63.7%.
Abstract:
To solve the task of Chinese zero pronoun identification,this paper proposes a Chinese zero pronoun identification model based on deep neural network. Firstly,attention mechanism is applied to learn more semantic information from the context of zero pronoun. Then,Tree-LSTM is used to capture syntactic structure features of the context of the zero pronoun. Finally,semantic information and syntactic structure information are combined to identify the zero pronoun. Compared with the previous zero pronoun identification methods,experiments on Chinese OntoNotes5.0 corpus show that our proposed approach can more effectively improve the recognition effect,and the F1 value reaches 63.7%.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2021-01-19.
基金项目:国家自然科学基金项目(61772278、61472191)、国家社科基金项目(18BYY127)和江苏高校哲学社会科学优秀创新团队建设项目.
通讯作者:曲维光,博士,教授,博士生导师,研究方向:自然语言处理. E-mail:wgqu_nj@163.com
更新日期/Last Update: 2021-12-15