File:Crowdsourcing Semantic Label Propagation in Relation Classification.pdf
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DescriptionCrowdsourcing Semantic Label Propagation in Relation Classification.pdf |
English: Distant supervision is a popular method for performing relation extraction from text that is known to produce noisy labels. Most progress in relation extraction and classification has been made with crowdsourced corrections to distant-supervised labels, and there is evidence that indicates still more would be better. In this paper, we explore the problem of propagating human annotation signals gathered for open-domain relation classification through the CrowdTruth methodology for crowdsourcing, that captures ambiguity in annotations by measuring inter-annotator disagreement. Our approach propagates annotations to sentences that are similar in a low dimensional embedding space, expanding the number of labels by two orders of magnitude. Our experiments show significant improvement in a sentence-level multi-class relation classifier. |
Date | |
Source | Content available at arXiv.org (link) (archive.org link) |
Author | Anca Dumitrache, Lora Aroyo, Chris Welty |
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