Biomedical named entity recognition and linking datasets: survey and our recent development

Ming-Siang Huang; Po-Ting Lai; Pei-Yen Lin; Yu-Ting You; Richard Tzong-Han Tsai; Wen-Lian Hsu

doi:10.1093/bib/bbaa054

Biomedical named entity recognition and linking datasets: survey and our recent development

Brief Bioinform. 2020 Dec 1;21(6):2219-2238. doi: 10.1093/bib/bbaa054.

Authors

Ming-Siang Huang¹, Po-Ting Lai², Pei-Yen Lin³, Yu-Ting You⁴, Richard Tzong-Han Tsai⁵, Wen-Lian Hsu⁴

Affiliations

¹ Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan.
² Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan.
³ Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan.
⁴ Intelligent Agent Systems Laboratory, Institute of Information Science, Academia Sinica, Taipei, Taiwan.
⁵ Intelligent Information Service Research Laboratory, Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan.

PMID: 32602538
DOI: 10.1093/bib/bbaa054

Abstract

Natural language processing (NLP) is widely applied in biological domains to retrieve information from publications. Systems to address numerous applications exist, such as biomedical named entity recognition (BNER), named entity normalization (NEN) and protein-protein interaction extraction (PPIE). High-quality datasets can assist the development of robust and reliable systems; however, due to the endless applications and evolving techniques, the annotations of benchmark datasets may become outdated and inappropriate. In this study, we first review commonlyused BNER datasets and their potential annotation problems such as inconsistency and low portability. Then, we introduce a revised version of the JNLPBA dataset that solves potential problems in the original and use state-of-the-art named entity recognition systems to evaluate its portability to different kinds of biomedical literature, including protein-protein interaction and biology events. Lastly, we introduce an ensembled biomedical entity dataset (EBED) by extending the revised JNLPBA dataset with PubMed Central full-text paragraphs, figure captions and patent abstracts. This EBED is a multi-task dataset that covers annotations including gene, disease and chemical entities. In total, it contains 85000 entity mentions, 25000 entity mentions with database identifiers and 5000 attribute tags. To demonstrate the usage of the EBED, we review the BNER track from the AI CUP Biomedical Paper Analysis challenge. Availability: The revised JNLPBA dataset is available at https://iasl-btm.iis.sinica.edu.tw/BNER/Content/Re vised_JNLPBA.zip. The EBED dataset is available at https://iasl-btm.iis.sinica.edu.tw/BNER/Content/AICUP _EBED_dataset.rar. Contact: Email: thtsai@g.ncu.edu.tw, Tel. 886-3-4227151 ext. 35203, Fax: 886-3-422-2681 Email: hsu@iis.sinica.edu.tw, Tel. 886-2-2788-3799 ext. 2211, Fax: 886-2-2782-4814 Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

Keywords: biological information retrieval; biomedical dataset; biomedical natural language processing; named entity recognition.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Benchmarking
Computational Biology / methods
Data Mining* / methods
Databases, Factual
Information Storage and Retrieval*
Natural Language Processing*
Neural Networks, Computer
PubMed
Software
Surveys and Questionnaires