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In this paper, we propose "distributed Bayesian linkage" or d-blink -- the first scalable and distributed end-to-end Bayesian model for ER, which propagates uncertainty in blocking, matching and merging.
We evaluate STANCE's ability to detect whether two strings can refer to the same entity--a task we term alias detection.
Knowledge bases (KBs) store rich yet heterogeneous entities and facts.
We tackle optimization of weighted set packing by relaxing integrality in our ILP formulation.
One of the key steps in language resource creation is the identification of the text segments to be annotated, or markables, which depending on the task may vary from nominal chunks for named entity resolution to (potentially nested) noun phrases in coreference resolution (or mentions) to larger text segments in text segmentation.