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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (5): 40-47    DOI: 10.11925/infotech.2096-3467.2017.1302
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DBLC Model for Word Segmentation Based on Autonomous Learning
Guoming Feng,Xiaodong Zhang(),Suhui Liu
School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
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Abstract  

[Objective] This paper tries to improve the accuracy of word segmentation for literature with lots of scientific terms. [Methods] First, we programed the DBLC model, which combined the methods of dictionary, statistics and deep learning. Then, we retrieved articles from the Chinese Management Case Center to build the experimental corpus. Finally, we compared the performance of this new model with the existing ones. [Results] The performance of the DBLC model was better than others. Its word segmentation accuracy was up to 96.3%. [Limitations] We did not separate the words of the original dictionary from the new words. We did not re-design the storage structure of the dictionary, which prolonged the computing time of our model. [Conclusions] The proposed DBLC model improves the accuracy of word segmentation, which is also positively co-related to the dictionary size.

Key wordsChinese Word Segmentation      Sequence Labeling      BI-LSTM-CRF      Autonomous Learning      Word Segmentation Based on Dictionary     
Received: 21 December 2017      Published: 20 June 2018

Cite this article:

Guoming Feng,Xiaodong Zhang,Suhui Liu. DBLC Model for Word Segmentation Based on Autonomous Learning. Data Analysis and Knowledge Discovery, 2018, 2(5): 40-47.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1302     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I5/40

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