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C-Norm: a neural approach to few-shot entity normalization

Abstract : Background: Entity 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. Results: Our 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. Conclusions: Our 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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Soumis le : mercredi 6 janvier 2021 - 16:02:17
Dernière modification le : mardi 16 novembre 2021 - 04:27:41


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Arnaud Ferré, Louise Deléger, Robert Bossy, Pierre Zweigenbaum, Claire Nédellec. C-Norm: a neural approach to few-shot entity normalization. BMC Bioinformatics, BioMed Central, 2020, 21 (S23), pp.1-19. ⟨10.1186/s12859-020-03886-8⟩. ⟨hal-03100410⟩



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