SBTA reformulates topic modeling to assign topics at the segment level rather than document level, yielding cleaner topics on a new SemEval-STM dataset created via LLM decomposition and human refinement.
Proceedings of The Web Conference 2020 , pages =
4 Pith papers cite this work. Polarity classification is still indexing.
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Polaris separates semantic meaning from hierarchical structure in embeddings via angular geometry and radius on a hypersphere, yielding up to 19-point gains in taxonomy expansion retrieval over baselines.
A deep Q-learning algorithm solves the fairness-aware profit maximization problem on social networks and reports up to 10 times higher profit than baselines on real datasets while meeting community fairness thresholds.
citing papers explorer
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From Documents to Segments: A Contextual Reformulation for Topic Assignment
SBTA reformulates topic modeling to assign topics at the segment level rather than document level, yielding cleaner topics on a new SemEval-STM dataset created via LLM decomposition and human refinement.
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Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning
Polaris separates semantic meaning from hierarchical structure in embeddings via angular geometry and radius on a hypersphere, yielding up to 19-point gains in taxonomy expansion retrieval over baselines.
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Fairness-Aware Profit Maximization using Deep Reinforcement Learning
A deep Q-learning algorithm solves the fairness-aware profit maximization problem on social networks and reports up to 10 times higher profit than baselines on real datasets while meeting community fairness thresholds.