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.2017 Oct 30;18(1):462.
doi: 10.1186/s12859-017-1868-5.

Long short-term memory RNN for biomedical named entity recognition

Affiliations

Long short-term memory RNN for biomedical named entity recognition

Chen Lyu et al. BMC Bioinformatics..

Abstract

Background: Biomedical 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.

Results: We 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.

Conclusions: Our 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 .

Keywords: Biomedical named entity recognition; Character representation; LSTM; Recurrent neural network; Word embeddings.

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The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
The model architecture
Fig. 2
Fig. 2
Effects of character representation. +Char — with character representation; -Char — without character representation.a LSTM-RNN,b BLSTM-RNN
Fig. 3
Fig. 3
Feature representation of our model. Each column indicates the feature representation from BLSTM for each token. Each grid in the column indicates each dimension of the feature representation. The dimension of the feature representation is 100
Fig. 4
Fig. 4
Feature representation of the word “factor”. “factor1” is the word in the first sentence. “factor2” and “factor3” are the corresponding words in the second sentence. Each vertical bar indicates one dimension of the feature representation for the corresponding word
See this image and copyright information in PMC

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References

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