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        <title>Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data</title>
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    <name type="personal">
        <namePart type="given">Natalie</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
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        <name type="personal">
            <namePart type="given">Rebecca</namePart>
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    <abstract>Crowdsourcing offers a convenient means of obtaining labeled data quickly and inexpensively. However, crowdsourced labels are often noisier than expert-annotated data, making it difficult to aggregate them meaningfully. We present an aggregation approach that learns a regression model from crowdsourced annotations to predict aggregated labels for instances that have no expert adjudications. The predicted labels achieve a correlation of 0.594 with expert labels on our data, outperforming the best alternative aggregation method by 11.9%. Our approach also outperforms the alternatives on third-party datasets.</abstract>
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    <identifier type="doi">10.18653/v1/D17-1204</identifier>
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%0 Conference Proceedings
%T Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data
%A Parde, Natalie
%A Nielsen, Rodney
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F parde-nielsen-2017-finding
%X Crowdsourcing offers a convenient means of obtaining labeled data quickly and inexpensively. However, crowdsourced labels are often noisier than expert-annotated data, making it difficult to aggregate them meaningfully. We present an aggregation approach that learns a regression model from crowdsourced annotations to predict aggregated labels for instances that have no expert adjudications. The predicted labels achieve a correlation of 0.594 with expert labels on our data, outperforming the best alternative aggregation method by 11.9%. Our approach also outperforms the alternatives on third-party datasets.
%R 10.18653/v1/D17-1204
%U https://aclanthology.org/D17-1204
%U https://doi.org/10.18653/v1/D17-1204
%P 1907-1912

Markdown (Informal)

[Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data](https://aclanthology.org/D17-1204) (Parde & Nielsen, EMNLP 2017)

• Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data (Parde & Nielsen, EMNLP 2017)

ACL

• Natalie Parde and Rodney Nielsen. 2017. Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1907–1912, Copenhagen, Denmark. Association for Computational Linguistics.