A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles.
Database (Oxford)
; 20222022 07 15.
Artículo
en Inglés
| MEDLINE | ID: covidwho-1948247
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/.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Minería de Datos
/
COVID-19
Tipo de estudio:
Estudio experimental
/
Estudio pronóstico
/
Revisiones
Límite:
Humanos
Idioma:
Inglés
Año:
2022
Tipo del documento:
Artículo
País de afiliación:
Database
Similares
MEDLINE
...
LILACS
LIS