Formalizes batched entity resolution with an NP-hardness proof for optimal batch selection, gives an optimal algorithm under a condition on entity sizes, and reports superior recall over baselines on six datasets.
Deep entity matching with pre-trained language models
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Alper unifies entity resolution matching and clustering into an iterative graph refinement and probabilistic label propagation process that adaptively selects LLM queries via a budgeted greedy optimization to outperform cascaded pipelines on eight benchmarks.
A decomposition-first pipeline with topology-aware chunking and interface-constrained merging converts full clinical guidelines into executable decision graphs, raising edge precision from 19.6% to 69.0% and triplet recall from 16.1% to 87.5% on a prostate guideline benchmark.
citing papers explorer
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Entity Resolution via Batched Oracle Queries
Formalizes batched entity resolution with an NP-hardness proof for optimal batch selection, gives an optimal algorithm under a condition on entity sizes, and reports superior recall over baselines on six datasets.
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Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution
Alper unifies entity resolution matching and clustering into an iterative graph refinement and probabilistic label propagation process that adaptively selects LLM queries via a budgeted greedy optimization to outperform cascaded pipelines on eight benchmarks.
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Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs
A decomposition-first pipeline with topology-aware chunking and interface-constrained merging converts full clinical guidelines into executable decision graphs, raising edge precision from 19.6% to 69.0% and triplet recall from 16.1% to 87.5% on a prostate guideline benchmark.