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

Xuan Wang, Yu Zhang, Xiang Ren, Yuhao Zhang, Marinka Zitnik, Jingbo Shang, Curtis Langlotz, Jiawei Han

Research output: Contribution to journalArticle

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 wordlevel information among relevant biomedical entities across differently labeled corpora.

Original languageEnglish (US)
Pages (from-to)1745-1752
Number of pages8
JournalBioinformatics
Volume35
Issue number10
DOIs
StatePublished - May 15 2019

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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  • Cite this

    Wang, X., Zhang, Y., Ren, X., Zhang, Y., Zitnik, M., Shang, J., Langlotz, C., & Han, J. (2019). Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics, 35(10), 1745-1752. https://doi.org/10.1093/bioinformatics/bty869