BioBERT: a pre-trained biomedical language representation model for biomedical text mining.
Bioinformatics
; 36(4): 1234-1240, 2020 02 15.
Article
en En
| MEDLINE
| ID: mdl-31501885
ABSTRACT
MOTIVATION Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. RESULTS:
We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. AVAILABILITY AND IMPLEMENTATION We make the pre-trained weights of BioBERT freely available at https//github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https//github.com/dmis-lab/biobert.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento de Lenguaje Natural
/
Minería de Datos
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2020
Tipo del documento:
Article