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Communication Dans Un Congrès Année : 2020

Divide to Better Classify

Résumé

Medical information is present in various text-based resources such as electronic medical records, biomedical literature, social media, etc. Using all these sources to extract useful information is a real challenge. In this context, the single-label classification of texts is an important task. Recently, in-depth classifiers have shown their ability to achieve very good results. However, their results generally depend on the amount of data used during the training phase. In this article, we propose a new approach to increase text data. We have compared this approach for 5 real data sets with the main approaches in the literature and our proposal outperforms in all configurations.
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Dates et versions

hal-04174551 , version 1 (01-08-2023)

Identifiants

Citer

Yves Mercadier, Jérôme Azé, Sandra Bringay. Divide to Better Classify. AIME 2020 - 18th International Conference on Artificial Intelligence in Medicine, Oct 2020, Minneapolis, United States. pp.89-99, ⟨10.1007/978-3-030-59137-3_9⟩. ⟨hal-04174551⟩
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