点击次数：

论文类型：期刊论文

发表时间：2017-11-24

发表刊物：Bioinformatics (Oxford, England)

收录刊物：SCIE、PubMed

卷号：34

期号：8

页面范围：1381-1388

ISSN号：1367-4811

摘要：Motivation: In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction. However, most popular chemical NER methods are based on traditional machine learning and their performances are heavily dependent on the feature engineering. Moreover, these methods are sentence-level ones which have the tagging non-consistency problem.; Results: In this paper, we propose a neural network approach, i.e., attention-based bidirectional Long Short-Term Memory with a conditional random field layer (Att-BiLSTM-CRF), to document-level chemical NER. The approach leverages document-level global information obtained by attention mechanism to enforce tagging consistency across multiple instances of the same token in a document. It achieves better performances with little feature engineering than other state-of-the-art methods on the BioCreative IV chemical compound and drug name recognition (CHEMDNER) corpus and the BioCreative V chemical-disease relation (CDR) task corpus (the F-scores of 91.14% and 92.57%, respectively).; Availability: Data and code are available at https://github.com/lingluodlut/Att-ChemdNER.; Contact: yangzh@dlut.edu.cn.; Supplementary information: Supplementary data are available at Bioinformatics online.