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Entity, Relation, and Event Extraction with Contextualized ...

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processingand the 9th International Joint Conference on Natural Language Processing, pages 5784 5789,Hong Kong, China, November 3 7, 2019 Association for Computational Linguistics5784 Entity, Relation, and Event Extractionwith Contextualized Span RepresentationsDavid Wadden Ulme Wennberg Yi Luan Hannaneh Hajishirzi Paul G. Allen School of Computer Science & Engineering, University of Washington Google AI Language Allen Institute for Artificial examine the capabilities of a unified, multi-task framework for three information extrac-tion tasks: named entity recognition, rela-tion Extraction , and Event (called DYGIE++) accomplishesall tasks by enumerating, refining, and scoringtext spans designed to capture local (within-sentence) and global (cross-sentence) framework achieves state-of-the-art results across all tasks, on four datasetsfrom a variety of domains.

named entity recognition, relation extraction, event extraction, and coreference resolution – can benefit from incorporating global context across sentences or from non-local dependencies among phrases. For example, knowledge of a coreference relation-ship can provide information to help infer the type of a difficult-to-classify entity mention.

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