ArticleFrontiers in research metrics and analytics2021
Ensemble of Deep Masked Language Models for Effective Named Entity Recognition in Health and Life Science Corpora.
Article in Frontiers in research metrics and analytics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The promise and peril of AI for disability health equity: A justice-centered narrative review.PLOS digital health · 2026Review
- Enhancing the SIB Literature Services (SIBiLS) with annotations to support biocuration.Database : the journal of biological databases and curation · 2026Article
- Collaborative intelligence in AI: Evaluating the performance of a council of AIs on the USMLE.PLOS digital health · 2025Article
- Fine-tuning of language models for automated structuring of medical exam reports to improve patient screening and analysis.Scientific reports · 2025Article
- A Dataset for Evaluating Contextualized Representation of Biomedical Concepts in Language Models.Scientific data · 2024Article
- Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey.Yearbook of medical informatics · 2023Article
- Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature.Systematic reviews · 2023Article
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Authors and funding
5 authors.
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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 improvement of the ensemble models over an individual BERT-based baseline model, with an overall best performance of 77% macro F1-score. We further perform a detailed analysis of the ensemble results and show how their effectiveness changes according to entity properties, such as length, corpus frequency, and annotation consistency. The results suggest that the ensembles of deep masked language models are an effective strategy for tackling NER across corpora from the health and life science domains.
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