Team LIA/LS2N at BioCreative VII LitCovid Track: Multi-label Document Classification for COVID-19 Literature using Keyword Based Enhancement and Few-Shot Learning - Archive ouverte HAL Access content directly
Conference Papers Year : 2021

Team LIA/LS2N at BioCreative VII LitCovid Track: Multi-label Document Classification for COVID-19 Literature using Keyword Based Enhancement and Few-Shot Learning

Yanis Labrak

Abstract

Multi-label text classification consists in attributing, for each textual document, one or more labels. Due to its nature, the task is often considered to be more challenging than other types of classification problems since the number of labels to assign is unknown. In text documents, this difficulty is generally the result of a blurry border between lexical fields of the labels or an underrepresentation of some of them. In this paper, we seek to automatically associate categories to scientific articles related to the COVID-19. We propose to address this multi-label classification problem by integrating an original keyword enhancement method to the TARS transformer-based approach designed to perform few-shot learning. Experiments conducted during the BioCreative challenge on the multi-label classification task show that our approach outperforms the baseline (ML-Net), no matter the metric considered.
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Dates and versions

hal-03426326 , version 1 (12-11-2021)

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  • HAL Id : hal-03426326 , version 1

Cite

Yanis Labrak, Richard Dufour. Team LIA/LS2N at BioCreative VII LitCovid Track: Multi-label Document Classification for COVID-19 Literature using Keyword Based Enhancement and Few-Shot Learning. BioCreative VII Challenge Evaluation Workshop, Nov 2021, Virtual Conference, United States. ⟨hal-03426326⟩
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