Long short-term memory RNN for biomedical named entity recognition

BMC Bioinformatics

Research Article

Bo Chen¹  Yafeng Ren²  Donghong Ji³  Chen Lyu³ 

[1] Department of Chinese Language & Literature, Hubei University of Art & Science, 24105, Xiangyang, Hubei, China;Guangdong Collaborative Innovation Center for Language Research & Services, Guangdong University of Foreign Studies, 510420, Guangzhou, Guangdong, China;School of Computer Science, Wuhan University, 430072, Wuhan, Hubei, China;

关键词: Biomedical named entity recognition;    Word embeddings;    Character representation;    Recurrent neural network;    LSTM;   

DOI  :  10.1186/s12859-017-1868-5

 received in 2016-12-21, accepted in 2017-10-16,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundBiomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for this task. However, these state-of-the-art BNER systems largely depend on hand-crafted features.ResultsWe present a recurrent neural network (RNN) framework based on word embeddings and character representation. On top of the neural network architecture, we use a CRF layer to jointly decode labels for the whole sentence. In our approach, contextual information from both directions and long-range dependencies in the sequence, which is useful for this task, can be well modeled by bidirectional variation and long short-term memory (LSTM) unit, respectively. Although our models use word embeddings and character embeddings as the only features, the bidirectional LSTM-RNN (BLSTM-RNN) model achieves state-of-the-art performance — 86.55% F1 on BioCreative II gene mention (GM) corpus and 73.79% F1 on JNLPBA 2004 corpus.ConclusionsOur neural network architecture can be successfully used for BNER without any manual feature engineering. Experimental results show that domain-specific pre-trained word embeddings and character-level representation can improve the performance of the LSTM-RNN models. On the GM corpus, we achieve comparable performance compared with other systems using complex hand-crafted features. Considering the JNLPBA corpus, our model achieves the best results, outperforming the previously top performing systems. The source code of our method is freely available under GPL at https://github.com/lvchen1989/BNER.

【 授权许可】

CC BY   
© The Author(s) 2017

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