Joint Entity Resolution

Abstract Entity resolution (ER) is the process of matching records that represent the same real-world entity and then merging them. We consider the ER problem for two related datasets. In the datasets, a record in one can refer to a record in the other and an ER process running on one set can affect an ER process on the other. We formalize the joint ER model for datasets which reference each other by treating the match and merge functions as black boxes. We identify important properties for match and merge functions that, if satisfied, allow much more efficient ER. We provide four algorithms that run Entity Resolution for a pair of datasets. We show that our parallel algorithms require shorter runtime than naive alternate algorithms. We also introduce improvements for our parallel algorithms which result in fewer feature comparisons

Similarity-based Memory Enhanced Joint Entity and Relation Extraction

Document-level joint entity and relation extraction is a challenging information extraction problem that requires a unified approach where a single neural network performs four sub-tasks: mention detection, coreference resolution, entity classification, and relation extraction. Existing methods often utilize a sequential multi-task learning approach, in which the arbitral decomposition causes the current task to depend only on the previous one, missing the possible existence of the more complex relationships between them. In this paper, we present a multi-task learning framework with bidirectional memory-like dependency between tasks to address those drawbacks and perform the joint problem more accurately. Our empirical studies show that the proposed approach outperforms the existing methods and achieves state-of-the-art results on the BioCreative V CDR corpus