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Comput Biol Med. 2019 May;108:122-132. doi: 10.1016/j.compbiomed.2019.04.002. Epub 2019 Apr 7.

Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition.

Author information

1
Department of Computer Science, Guangdong University of Technology, Guangzhou, China. Electronic address: kaixu.gdut@foxmail.com.
2
Department of Computer Science, Guangdong University of Technology, Guangzhou, China; Department of Computer Science, City University of Hong Kong, Hong Kong, China. Electronic address: zhengyang5-c@my.cityu.edu.hk.
3
Department of Computer Science, Guangdong University of Technology, Guangzhou, China. Electronic address: ppkanggdut@126.com.
4
Department of Computer Science, Guangdong University of Technology, Guangzhou, China. Electronic address: wangqi_6414@sina.com.
5
Department of Computer Science, Guangdong University of Technology, Guangzhou, China. Electronic address: liuwy@gdut.edu.cn.

Abstract

BACKGROUND:

Disease named entity recognition (NER) plays an important role in biomedical research. There are a significant number of challenging issues to be addressed; among these, the identification of rare diseases and complex disease names and the problem of tagging inconsistency (i.e., if an entity is tagged differently in a document) are attracting substantial research attention.

METHODS:

We propose a new neural network method named Dic-Att-BiLSTM-CRF (DABLC) for disease NER. DABLC applies an efficient exact string matching method to match disease entities with a disease dictionary; here, the dictionary is constructed based on the Disease Ontology. Furthermore, DABLC constructs a dictionary attention layer by incorporating a disease dictionary matching method and document-level attention mechanism. Finally, a bidirectional long short-term memory network and conditional random field (BiLSTM-CRF) with a dictionary attention layer is proposed to combine the disease dictionary to develop disease NER.

RESULTS:

Extensive experiments are conducted on two widely-used corpora: the NCBI disease corpus and the BioCreative V CDR corpus. We apply each test on 10 executions of each model, with a 95% confidence interval. DABLC achieves the highest F1 scores (NCBI: Precision = 0.883, Recall = 0.89, F1 = 0.886; BioCreative V CDR: Precision = 0.891, Recall = 0.875, F1 = 0.883), outperforming the state-of-the-art methods.

CONCLUSION:

DABLC combines the advantages of both external dictionary resources and deep attention neural networks. This aids the identification of rare diseases and complex disease names; moreover, it reduces the impact of tagging inconsistency. Special disease NER and deep learning models addressing long sentences are noteworthy areas for future examination.

KEYWORDS:

Biomedical informatics; Machine learning; Named entity recognition; Neural network; String matching

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