| Literature DB >> 32422651 |
Sendong Zhao1, Chang Su2, Zhiyong Lu3, Fei Wang1.
Abstract
The recent years have witnessed a rapid increase in the number of scientific articles in biomedical domain. These literature are mostly available and readily accessible in electronic format. The domain knowledge hidden in them is critical for biomedical research and applications, which makes biomedical literature mining (BLM) techniques highly demanding. Numerous efforts have been made on this topic from both biomedical informatics (BMI) and computer science (CS) communities. The BMI community focuses more on the concrete application problems and thus prefer more interpretable and descriptive methods, while the CS community chases more on superior performance and generalization ability, thus more sophisticated and universal models are developed. The goal of this paper is to provide a review of the recent advances in BLM from both communities and inspire new research directions.Entities:
Keywords: Biomedical Literature Mining; Deep Learning; Natural Language Processing
Mesh:
Year: 2021 PMID: 32422651 PMCID: PMC8138828 DOI: 10.1093/bib/bbaa057
Source DB: PubMed Journal: Brief Bioinform ISSN: 1467-5463 Impact factor: 11.622