Crowdsourcing Semantic Label Propagation in Relation Classification
Anca Dumitrache, Lora M. Aroyo, Chris Welty · 2018
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 sentencelevel multi-class relation classifier.