Cross-type biomedical named entity recognition with deep multi-task learning

• Published in: Bioinformatics Vol. 35; no. 10; pp. 1745 - 1752
• Main Authors: Wang, Xuan, Zhang, Yu, Ren, Xiang, Zhang, Yuhao, Zitnik, Marinka, Shang, Jingbo, Langlotz, Curtis, Han, Jiawei
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15.05.2019
• Subjects: Benchmarking; bioinformatics; data collection; Deep Learning; Neural Networks, Computer; Software
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/bty869

Abstract Motivation State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performance remains limited by the available training data for each entity type. Results We propose a multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them. In experiments on 15 benchmark BioNER datasets, our multi-task model achieves substantially better performance compared with state-of-the-art BioNER systems and baseline neural sequence labeling models. Further analysis shows that the large performance gains come from sharing character- and word-level information among relevant biomedical entities across differently labeled corpora. Availability and implementation Our source code is available at https://github.com/yuzhimanhua/lm-lstm-crf. Supplementary information Supplementary data are available at Bioinformatics online.