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arXiv 1606.033982016-06cs.CL

Bootstrapping Distantly Supervised IE using Joint Learning and Small Well-structured Corpora

Carnegie Mellon University（卡内基梅隆大学）

Lidong Bing, Bhuwan Dhingra, Kathryn Mazaitis, Jong Hyuk Park, William W. Cohen

备注：: 10 pages, 5 figures

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We propose a framework to improve performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction. We combine this with a novel use of document structure: in some small, well-structured corpora, sections can be identified that correspond to relation arguments, and distantly-labeled examples from such sections tend to have good precision. Using these as seeds we extract additional relation examples by applying label propagation on a graph composed of noisy examples extracted from a large unstructured testing corpus. Combined with the soft constraint that concept examples should have the same type as the second argument of the relation, we get significant improvements over several state-of-the-art approaches to distantly-supervised relation extraction.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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