Ensemble of Deep Masked Language Models for Effective Named Entity Recognition in Health and Life Science Corpora.

ABSTRACT: The health and life science domains are well known for their wealth of named entities found in large free text corpora, such as scientific literature and electronic health records. To unlock the value of such corpora, named entity recognition (NER) methods are proposed. Inspired by the success of transformer-based pretrained models for NER, we assess how individual and ensemble of deep masked language models perform across corpora of different health and life science domains-biology, chemistry, and medicine-available in different languages-English and French. Individual deep masked language models, pretrained on external corpora, are fined-tuned on task-specific domain and language corpora and ensembled using classical majority voting strategies. Experiments show statistically significant

SUBMITTER: Naderi N 

PROVIDER: S-EPMC8640190 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature