A lightweight mixup-based short texts clustering for contrastive learning
Qiang Xu,
HaiBo Zan,
ShengWei Ji
Abstract:Traditional text clustering based on distance struggles to distinguish between overlapping representations in medical data. By incorporating contrastive learning, the feature space can be optimized and applies mixup implicitly during the data augmentation phase to reduce computational burden. Medical case text is prevalent in everyday life, and clustering is a fundamental method of identifying major categories of conditions within vast amounts of unlabeled text. Learning meaningful clustering scores in data re… Show more
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