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        <title>The Benefits of a Model of Annotation</title>
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    <abstract>Standard agreement measures for interannotator reliability are neither necessary nor sufficient to ensure a high quality corpus. In a case study of word sense annotation, conventional methods for evaluating labels from trained annotators are contrasted with a probabilistic annotation model applied to crowdsourced data. The annotation model provides far more information, including a certainty measure for each gold standard label; the crowdsourced data was collected at less than half the cost of the conventional approach.</abstract>
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%0 Journal Article
%T The Benefits of a Model of Annotation
%A Passonneau, Rebecca J.
%A Carpenter, Bob
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F passonneau-carpenter-2014-benefits
%X Standard agreement measures for interannotator reliability are neither necessary nor sufficient to ensure a high quality corpus. In a case study of word sense annotation, conventional methods for evaluating labels from trained annotators are contrasted with a probabilistic annotation model applied to crowdsourced data. The annotation model provides far more information, including a certainty measure for each gold standard label; the crowdsourced data was collected at less than half the cost of the conventional approach.
%R 10.1162/tacl_a_00185
%U https://aclanthology.org/Q14-1025
%U https://doi.org/10.1162/tacl_a_00185
%P 311-326

Markdown (Informal)

[The Benefits of a Model of Annotation](https://aclanthology.org/Q14-1025) (Passonneau & Carpenter, TACL 2014)

• The Benefits of a Model of Annotation (Passonneau & Carpenter, TACL 2014)

ACL

• Rebecca J. Passonneau and Bob Carpenter. 2014. The Benefits of a Model of Annotation. Transactions of the Association for Computational Linguistics, 2:311–326.