Domain-specific Chinese Term Extraction via Word Segmentation Optimization

2015 ◽  
Vol 12 (17) ◽  
pp. 6477-6490
Author(s):  
Chuyuan Wei
2011 ◽  
Author(s):  
Meng Zhang ◽  
Xiaojun Lin ◽  
Xu Dai ◽  
Xihong Wu
Keyword(s):  

2010 ◽  
Vol 13 (1) ◽  
pp. 115-125 ◽  
Author(s):  
Daniel Zeng ◽  
Donghua Wei ◽  
Michael Chau ◽  
Feiyue Wang

Author(s):  
Lujun Zhao ◽  
Qi Zhang ◽  
Peng Wang ◽  
Xiaoyu Liu

Most existing Chinese word segmentation (CWS) methods are usually supervised. Hence, large-scale annotated domain-specific datasets are needed for training. In this paper, we seek to address the problem of CWS for the resource-poor domains that lack annotated data. A novel neural network model is proposed to incorporate unlabeled and partially-labeled data. To make use of unlabeled data, we combine a bidirectional LSTM segmentation model with two character-level language models using a gate mechanism. These language models can capture co-occurrence information. To make use of partially-labeled data, we modify the original cross entropy loss function of RNN. Experimental results demonstrate that the method performs well on CWS tasks in a series of domains.


Author(s):  
Xiaoming Chen ◽  
Xuening Li ◽  
Yi Hu ◽  
Ruzhan Lu
Keyword(s):  

Author(s):  
Yuhang Yang ◽  
Tiejun Zhao ◽  
Qin Lu ◽  
Dequan Zheng ◽  
Hao Yu

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 12993-13002 ◽  
Author(s):  
Dangguo Shao ◽  
Na Zheng ◽  
Zhaoqiang Yang ◽  
Zhenhua Chen ◽  
Yan Xiang ◽  
...  

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