A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles. (15th July 2022)

• Record Type: Journal Article
• Authors: Lin, Sheng-Jie; Yeh, Wen-Chao; Chiu, Yu-Wen; Chang, Yung-Chun; Hsu, Min-Huei; Chen, Yi-Shin; Hsu, Wen-Lian
• Is Part Of: Database. Volume 2022(2022)
• Publication Date: 2022-07-15
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/baac056
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract: In this research, we explored various state-of-the-art biomedical-specific pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for the National Library of Medicine - Chemistry (NLM CHEM) and LitCovid tracks in the BioCreative VII Challenge, and propose a BERT-based ensemble learning approach to integrate the advantages of various models to improve the system's performance. The experimental results of the NLM-CHEM track demonstrate that our method can achieve remarkable performance, with F1 -scores of 85% and 91.8% in strict and approximate evaluations, respectively. Moreover, the proposed Medical Subject Headings identifier (MeSH ID) normalization algorithm is effective in entity normalization, which achieved a F1 -score of about 80% in both strict and approximate evaluations. For the LitCovid track, the proposed method is also effective in detecting topics in the Coronavirus disease 2019 (COVID-19) literature, which outperformed the compared methods and achieve state-of-the-art performance in the LitCovid corpus. Database URL : https://www.ncbi.nlm.nih.gov/research/coronavirus/ .