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Findings support the hypothesis that unsupervised contextual re-ranking can significantly improve text retrieval effectiveness and suggest that the underlying techniques are modality-agnostic.
This work introduces MARETopic, a training-free framework that casts topic discovery as rank-based prototype selection and leads topic coherence on two of three datasets, ahead of both neural and clustering-based topic models.
This paper proposes a framework that combines neighbor embedding projections with rank-based manifold learning through rank aggregation, which can improve retrieval effectiveness in several scenarios, particularly when the baseline representation struggles to achieve high precision values.
GRaCE surpasses RaDE and Original Features across diverse datasets, including textual and image collections, excelling in retrieval, classification, and clustering tasks, considering state-of-the-art Transformer models as feature descriptors and Graph Convolutional Networks models in classification tasks.
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