Exploring the effects of drug, disease, and protein dependencies on biomedical named entity recognition: A comparative analysis - Han, Peifu; Li, Xue; Wang, Xun; Wang, Shuang; Gao, Changnan; Chen, Wenqi (Frontiers in Pharmacology, 2022)
Background: Biomedical Named Entity Recognition is one of the important tasks of biomedical literature mining. With the development of natural language processing technology, many deep learning models are used to extract valuable information from biomedical literature, which promotes the development of effective BioNER models. However, for specialized domains with diverse and complex contexts and a richer set of semantically related entity types (e.g., drug molecules, targets, pathways, etc. in the biomedical domain), whether the dependencies of these drugs, diseases, targets can be helpful still needs to be explored. Method: Providing additional dependency information beyond context, a method based on graph attention network and Bert pre-training model named MKGAT is proposed to improve BioNER performance in the biomedical domain. To enhance BioNER by using external dependency knowledge. We integrate BERT-processed text embeddings and entity dependencies to construct better entity embedding representations for biomedical named entity recognition. Results: The proposed method achieves the best performance on two datasets, NCBI-disease and BC2GM, with Precision of 90.71% and 88.19%, Recall of 92.52% and 88.05%, F1-scores of 91.61% and 88.12%, respectively, which performs better than existing methods. Conclusion: Drug, disease, and protein dependencies can allow entities to be better represented in neural networks, thereby improving the performance of BIONER.