Papers by Salah Uddin

2 papers
An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering (2020.acl-main)

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Challenge: Existing approaches to cluster short text streams exploit short text in a batch way, but determine optimal batch size is difficult since we have no priori knowledge when the topics evolve.
Approach: They propose an online Semantic-enhanced Dirichlet Model for short sext stream clustering which integrates the word-occurance semantic information into a new graphical model and clusters each arriving short text automatically in an online way.
Outcome: The proposed model has better performance than state-of-the-art models on synthetic and real-world data sets.
Do Not Guess, Verify: Logic-Guided Adaptive Reasoning for Multimodal Misinformation Detection (2026.findings-acl)

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Challenge: Existing multimodal misinformation detection paradigms rely on passive aggregation of multimodal features and social signals.
Approach: They propose a verification-oriented framework that integrates large vision–language models into multimodal misinformation detection through explicit rationale-guided reasoning.
Outcome: The proposed framework outperforms state-of-the-art methods on multimodal misinformation detection benchmarks while significantly reducing computational cost.

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