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| Chinese Named Entity Recognition Based on Deep Neural Network |
| ZHANG Hainan1, WU Dayong1, LIU Yue1, CHENG Xueqi2 |
1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
2. Institute of Network Technology, ICT(YANTAI), CAS, Yantai, Shandong 264000, China |
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Abstract Chinese NER is challenged by the implicit word boundary, lack of capitalization, and the polysemy of a single character in different words. This paper proposes a novel character-word joint encoding method in a deep learning framework for Chinese NER. It decreases the effect of improper word segmentation and sparse word dictionary in word-only embedding, while improves the results in character-only embedding of context missing. Experiments on the corpus of the Chinese Peoples' Daily Newspaper in 1998 demonstrates a good results: at least 1.6%, 8% and 3% improvements, respectively, in location, person and organization recognition tasks compared with character or word features; and 96.8%, 94.6%, 88.6% in F1, respectively, on location, person and organization recognition tasks if integrated with part of speech feature.
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Received: 25 September 2015
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2017, Vol. 31 
