Improving deep learning method for biomedical named entity recognition by using entity definition information

BMC Bioinformatics

Xiaolong Wang¹  Shuai Chen¹  Qingcai Chen²  Ying Xiong²  Buzhou Tang²  Jun Yan³  Yi Zhou⁴ 

[1] Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, Shenzhen, China;Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, Shenzhen, China;Peng Cheng Laboratory, Shenzhen, China;Yidu Cloud (Beijing) Technology Co., Ltd, Beijing, China;Zhongshan School of Medicine, Sun Yat-Sen University, 510080, Guangzhou, China;

关键词: Biomedical named entity recognition;    Entity definition information;    Machine reading comprehension;    Span-level one-pass method;   

DOI  :  10.1186/s12859-021-04236-y

来源: Springer

【 摘 要 】

BackgroundBiomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in biomedical text and determines their entity type. To accelerate the development of biomedical NER techniques in Spanish, the PharmaCoNER organizers launched a competition to recognize pharmacological substances, compounds, and proteins. Biomedical NER is usually recognized as a sequence labeling task, and almost all state-of-the-art sequence labeling methods ignore the meaning of different entity types. In this paper, we investigate some methods to introduce the meaning of entity types in deep learning methods for biomedical NER and apply them to the PharmaCoNER 2019 challenge. The meaning of each entity type is represented by its definition information.Material and methodWe investigate how to use entity definition information in the following two methods: (1) SQuad-style machine reading comprehension (MRC) methods that treat entity definition information as query and biomedical text as context and predict answer spans as entities. (2) Span-level one-pass (SOne) methods that predict entity spans of one type by one type and introduce entity type meaning, which is represented by entity definition information. All models are trained and tested on the PharmaCoNER 2019 corpus, and their performance is evaluated by strict micro-average precision, recall, and F1-score.ResultsEntity definition information brings improvements to both SQuad-style MRC and SOne methods by about 0.003 in micro-averaged F1-score. The SQuad-style MRC model using entity definition information as query achieves the best performance with a micro-averaged precision of 0.9225, a recall of 0.9050, and an F1-score of 0.9137, respectively. It outperforms the best model of the PharmaCoNER 2019 challenge by 0.0032 in F1-score. Compared with the state-of-the-art model without using manually-crafted features, our model obtains a 1% improvement in F1-score, which is significant. These results indicate that entity definition information is useful for deep learning methods on biomedical NER.ConclusionOur entity definition information enhanced models achieve the state-of-the-art micro-average F1 score of 0.9137, which implies that entity definition information has a positive impact on biomedical NER detection. In the future, we will explore more entity definition information from knowledge graph.

【 授权许可】

CC BY