Crowdsourcing Semantic Label Propagation in Relation Classification

TL;DR

This paper introduces a method that uses crowdsourced annotations and semantic similarity to significantly expand relation labels in text, improving relation classification accuracy.

Contribution

It proposes a novel approach combining CrowdTruth crowdsourcing with semantic embedding to propagate labels, enhancing relation extraction.

Findings

Expanded labels by two orders of magnitude

Significant improvement in relation classifier performance

Effective handling of annotation ambiguity

Abstract

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.