|Table of Contents|

Identification of Chinese Zero Pronouns Based on Deep Learning(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2021年04期
Page:
19-26
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Identification of Chinese Zero Pronouns Based on Deep Learning
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)
Keywords:
deep learning; Chinese zero pronoun; zero pronoun identification; Tree-LSTM; attention
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2021.04.004
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%.

References:

[1] KIM Y J. Subject/object drop in the acquistion of korean:a cross-linguistic comparison[J]. Journal of East Asian Linguistics,2000,9(4):325-351.
[2]YIN Q Y,ZHANG Y,ZHANG W N,et al. Deep reinforcement learning for Chinese zero pronoun resolution[C]//Proceedings of 56th Annual Meeting of the Association for Computational linguistics. Melbourne:Association for Computational Linguistics,2018:569-578.
[3]YIN Q Y,ZHANG Y,ZHANG W N,et al. Zero pronoun resolution with attention-based neural network[C]//Proceedings of the 27th International Conference on Computational Linguistics. Santa Fe:Association for Computational Linguistics,2018:13-23.
[4]LIN P Q,YANG M. Hierarchical attention network with pairwise loss for Chinese zero pronoun resolution[C]//Proceedings of 34th AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press,2020:8352-8358.
[5]秦凯伟,孔芳,李培峰,等. 基于规则的中文零指代项识别研究[J]. 计算机科学,2012,39(10):278-281.
[6]ZHAO S H,HWEE T N. Identification and resolution of Chinese zero pronouns:a machine learning approach[C]//Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague:Association for Computational Linguistics,2007:541-550.
[7]KONG F,ZHOU G D. A tree kernel-based unified framework for Chinese zero anaphora resolution[C]//Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Cambridge:Association for Computational Linguistics,2010:882-891.
[8]CHEN C,NG V. Chinese zero pronoun resolution:an unsupervised approach combining ranking and integer linear programming[C]//Twenty-Eighth AAAI Conference on Artificial Intelligence. Quebec:AAAI Press,2014:1622-1628.
[9]CHEN C,NG V. Chinese zero pronoun resolution with deep neural networks[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin:Association for Computational Linguistics,2016:778-788.
[10]LIU B Q,DU X K,LIU M,et al. Resolving Chinese zero pronoun with word embedding[C]//National CCF Conference on Natural Language Processing and Chinese Computing. Dalian:Springer,2017:828-838.
[11]KONG F,ZHOU G D. Chinese zero pronoun resolution:a chain to chain approach[C]//National CCF Conference on Natural Language Processing and Chinese Computing. Dalian:Springer,2017:393-405.
[12]SONG L F,XU K,ZHANG Y,et al. ZPR2:joint zero pronoun recovery and resolution using multi-task learning and BERT[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online:Association for Computational Linguistics,2020:5429-5434.
[13]PRADHAN S,MOSCHITTI A,XUE N W,et al. CoNLL-2012 shared task:modeling multilingual unrestricted coreference in OntoNotes[C]//Proceedings of the Joint Conference on EMNLP and CoNLL:Shared Task. Stroudsburg:Association for Computational Linguistics,2012:1-40.
[14]TAI K S,SOCHER R,MANNING C D. Improved semantic representations from tree-structured long short-term memory networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. Beijing:Association for Computational Linguistics,2015:1556-1566.
[15]ERIGUCHI A,HASHIMOTO K,TSURUOKA Y. Tree-to-sequence attentional neural machine translation[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin:Association for Computational Linguistics,2016:823-833.

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Last Update: 2021-12-15