Ensemble pretrained language models to extract biomedical knowledge from literature.

ABSTRACT:

Objectives

The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking.

Materials and methods

For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and taxono

SUBMITTER: Li Z 

PROVIDER: S-EPMC11339500 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature