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

Ying Xiong, Shuai Chen, Buzhou Tang, Qingcai Chen, Xiaolong Wang, Jun Yan, Yi Zhou

Abstract: Biomedical 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.

External IDs:doi:10.1186/s12859-021-04236-y