Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive Blocking: Learning to Scale Up Record Linkage
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Streaming cross document entity coreference resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Large-scale cross-document coreference using distributed inference and hierarchical models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Local and global algorithms for disambiguation to Wikipedia
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A discriminative hierarchical model for fast coreference at large scale
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
LINDA: distributed web-of-data-scale entity matching
Proceedings of the 21st ACM international conference on Information and knowledge management
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Entity resolution, the task of automatically determining which mentions refer to the same real-world entity, is a crucial aspect of knowledge base construction and management. However, performing entity resolution at large scales is challenging because (1) the inference algorithms must cope with unavoidable system scalability issues and (2) the search space grows exponentially in the number of mentions. Current conventional wisdom has been that performing coreference at these scales requires decomposing the problem by first solving the simpler task of entity-linking (matching a set of mentions to a known set of KB entities), and then performing entity discovery as a post-processing step (to identify new entities not present in the KB). However, we argue that this traditional approach is harmful to both entity-linking and overall coreference accuracy. Therefore, we embrace the challenge of jointly modeling entity-linking and entity-discovery as a single entity resolution problem. In order to make progress towards scalability we (1) present a model that reasons over compact hierarchical entity representations, and (2) propose a novel distributed inference architecture that does not suffer from the synchronicity bottleneck which is inherent in map-reduce architectures. We demonstrate that more test-time data actually improves the accuracy of coreference, and show that joint coreference is substantially more accurate than traditional entity-linking, reducing error by 75%.