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        <title>Crowdsourcing Semantic Label Propagation in Relation Classification</title>
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        <namePart type="given">Anca</namePart>
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            <title>Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)</title>
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            <namePart type="given">James</namePart>
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            <namePart type="given">Arpit</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Crowdsourcing Semantic Label Propagation in Relation Classification
%A Dumitrache, Anca
%A Aroyo, Lora
%A Welty, Chris
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F dumitrache-etal-2018-crowdsourcing
%X 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.
%R 10.18653/v1/W18-5503
%U https://aclanthology.org/W18-5503
%U https://doi.org/10.18653/v1/W18-5503
%P 16-21

Markdown (Informal)

[Crowdsourcing Semantic Label Propagation in Relation Classification](https://aclanthology.org/W18-5503) (Dumitrache et al., EMNLP 2018)

• Crowdsourcing Semantic Label Propagation in Relation Classification (Dumitrache et al., EMNLP 2018)

ACL

• Anca Dumitrache, Lora Aroyo, and Chris Welty. 2018. Crowdsourcing Semantic Label Propagation in Relation Classification. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 16–21, Brussels, Belgium. Association for Computational Linguistics.