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.
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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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