ArticleBMC bioinformatics2020
C-Norm: a neural approach to few-shot entity normalization.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- MilkOligoThesaurus, a dataset of mammalian milk oligosaccharide synonyms.Data in brief · 2024Article
- TaeC: A manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature.PloS one · 2024Article
- Few-shot learning for medical text: A review of advances, trends, and opportunities.Journal of biomedical informatics · 2023Review
- An analysis of entity normalization evaluation biases in specialized domains.BMC bioinformatics · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundEntity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the latest machine learning-based approaches, which have difficulty handling highly multi-class and few-shot learning problems. To address this issue, we propose C-Norm, a new neural approach which synergistically combines standard and weak supervision, ontological knowledge integration and distributional semantics.
resultsOur approach greatly outperforms all methods evaluated on the Bacteria Biotope datasets of BioNLP Open Shared Tasks 2019, without integrating any manually-designed domain-specific rules.
conclusionsOur results show that relatively shallow neural network methods can perform well in domains that present highly multi-class and few-shot learning problems.
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Registered trials
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