Chinese Word Segmentation Based on Bi-GRU Integrating Dictionary Information
Authors: 
Rong Xiang, Shiqun YinAuthors Info & Claims
Abstract
Chinese word segmentation (CWS) is an important and essential pre-processing step for Chinese language processing tasks. To date, various models based on deep neural networks have been extensively applied in CWS. Most of them learn from large scale labeled data. However, these models typically lack the capability of processing rare words and OOV words. In this paper, we use character embedding and bigram embedding as the inputs of Bi-GRU model and construct a feature vector to capture dictionary information of characters. we use the multi-head attention mechanism to get the weight of different features which is the inputs of a parallel Bi-GRU network. To evaluate the performance of the proposed model, we conducted experiments on PKU and MSR datasets. The experimental results of datasets show that our model achieves the-state-of-art performance compared to several baselines.
References
[1]
Xue N. Chinese word segmentation as character tagging. Computational Linguistics and Chinese Language Processing, 8(1): 29--48. 2003.
[2]
Stephen A. Della Pietra, Stephen A. Della Pietra, Stephen A. Della Pietra. A maximum entropy approach to natural language processing[M]. MIT Press, 1996.
[3]
Fuchun Peng, Fangfang Feng, and Andrew McCallum. Chinese segmentation and new word detection using conditional random fields. In Proceedings of the 20th international conference on Computational Linguistics, page 562. Association for Computational Linguistics.2004.
[4]
Lafferty, J.; McCallum, A.; and Pereira, F. Conditional random fields: Probabilistic models for segmentingand labeling sequence data.2001.
[5]
Chung J, Gulcehre C, Cho K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling [J]. arXiv: Neural and Evolutionary Computing, 2014.
[6]
Yao Y, Huang Z. Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation[J]. 2016.
[7]
Wang C, Xu B. Convolutional Neural Network with Word Embeddings for Chinese Word Segmentation[J]. 2017.
[8]
Deng X, Sun Y. An improved embedding matching model for Chinese word segmentation[C]// 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD). 2018.
[9]
Chen, X.; Qiu, X.; Zhu, C.; and Huang, X. Gated recursive neural network for chinese word segmentation. In ACL (1), 1744--1753.2015a.
[10]
Chen, X.; Qiu, X.; Zhu, C.; Liu, P.; and Huang, X. Long short-term memory neural networks for chinese word segmentation. In EMNLP.2015b.
[11]
Chen, X.; Shi, Z.; Qiu, X.; and Huang, X. Adversarial multi-criteria learning for chinese word segmentation.In ACL.2017.
[12]
Cai D, Zhao H, Zhang Z, et al. Fast and Accurate Neural Word Segmentation for Chinese[J]. 2017.
[13]
Cai D, and Zhao H.Neural word segmentation learning for chinese. In ACL.2016.
[14]
Yang J, Zhang Y and Dong F.Neural word segmentation with rich pretraining. In ACL.2017.
[15]
Bao Z, Li S, Xu W. Neural domain adaptation for Chinese word segmentation[C]// 2017 International Conference on Asian Language Processing (IALP). IEEE, 2017.
[16]
Liu J, Wu F, Wu C, et al. Neural Chinese word segmentation with dictionary [J]. Neurocomputing, 2019, 338(APR.21): 46--54.
[17]
Zhou J, Wang J, Liu G. Multiple Character Embeddings for Chinese Word Segmentation[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. 2019.
[18]
Duan S, Zhao H. (2019). Attention Is All You Need for Chinese Word Segmentation.
[19]
Ma J, Ganchev K, Weiss D. State-of-the-art Chinese Word Segmentation with Bi-LSTMs[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.
[20]
Siqi Huang, Bo Yan, Dongmei Zhang. A Detection Algorithm of Malicious Domain Based on Deep Learning and Multi-Head Attention Mechanism[C].
[21]
Zhang M, Zhang Y, Fu G. Transition-Based Neural Word Segmentation[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.
Index Terms
- Chinese Word Segmentation Based on Bi-GRU Integrating Dictionary Information
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Published In
October 2020
552 pages
ISBN:9781450387835
DOI:10.1145/3436369
Copyright © 2020 ACM.
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- Beijing University of Technology
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 January 2021
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ICCPR 2020
ICCPR 2020: 2020 9th International Conference on Computing and Pattern Recognition
October 30 - November 1, 2020
Xiamen, China
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