An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
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基于注意力的 BiLSTM-CRF 文档级化学命名实体识别方法
DOI：
10.1093/bioinformatics/btx761
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发表时间：
2018-04-15
期刊：
影响因子：
5.8
通讯作者：
Wang, Jian
中科院分区：
文献类型：
--
作者：
Luo, Ling;Yang, Zhihao;Wang, Jian
动机：在生物医学研究中，化学是一类重要的实体，而化学命名实体识别(NER)是生物医学信息提取领域的一项重要任务。然而，大多数流行的化学神经网络方法都是基于传统的机器学习，其性能在很大程度上依赖于特征工程。结果：本文提出了一种基于注意的条件随机场双向长期短期记忆(ATT-BiLSTM-CRF)方法，并将其应用于文档级化学NER。该方法利用注意机制获得的文档级全局信息，在文档中同一令牌的多个实例上强制标签一致性。它在BioCreative IV化合物和药物名称识别(CHEMDNER)语料库和BioCreative V化学-疾病关系(CDR)任务语料库上取得了比其他最先进的方法更好的性能，几乎没有特征工程(F-Score分别为91.14和92.57%)。
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 inconsistency 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).