1.合肥工业大学 计算机与信息学院 情感计算与先进智能机器安徽省重点实验室 合肥 230009 2.Faculty of Engineering, University of Tokushima, Tokushima, Japan 770-8506
Biomedical Named Entity Recognition Based on Deep Conditional Random Fields
SUN Xiao1, SUN Chongyuan1, REN Fuji1,2
1.Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, School of Computer and Information, Hefei University of Technology, Hefei 230009 2.Faculty of Engineering, University of Tokushima, Tokushima, Japan 770-8506
Abstract：Biomedical named entity recognition is the fundamental and key step in bioinformatics. In this paper, a biomedical named entity recognition method based on deep conditional random fields is proposed. The deep conditional random fields of multi-layer structure are constructed by stacking the linear-chain conditional random fields and the optimal feature set is built by incremental learning strategy. Finally, error correction algorithm based on full name-abbreviation and error correction algorithm based on domain knowledge are adopted for further modifying the recognition results. Experiments are conducted on the biomedical named entity recognition corpus JNLPBA, and the results demonstrate the effectiveness of the proposed method.
孙晓， 孙重远，任福继. 基于深层条件随机场的生物医学命名实体识别*[J]. 模式识别与人工智能, 2016, 29(11): 997-1008.
SUN Xiao, SUN Chongyuan, REN Fuji. Biomedical Named Entity Recognition Based on Deep Conditional Random Fields. , 2016, 29(11): 997-1008.
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2016, Vol. 29 
