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        <title>Negation-Instance Based Evaluation of End-to-End Negation Resolution</title>
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        <namePart type="given">Elizaveta</namePart>
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    <name type="personal">
        <namePart type="given">Annemarie</namePart>
        <namePart type="family">Friedrich</namePart>
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    <name type="personal">
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    <abstract>In this paper, we revisit the task of negation resolution, which includes the subtasks of cue detection (e.g. “not”, “never”) and scope resolution. In the context of previous shared tasks, a variety of evaluation metrics have been proposed. Subsequent works usually use different subsets of these, including variations and custom implementations, rendering meaningful comparisons between systems difficult. Examining the problem both from a linguistic perspective and from a downstream viewpoint, we here argue for a negation-instance based approach to evaluating negation resolution. Our proposed metrics correspond to expectations over per-instance scores and hence are intuitively interpretable. To render research comparable and to foster future work, we provide results for a set of current state-of-the-art systems for negation resolution on three English corpora, and make our implementation of the evaluation scripts publicly available.</abstract>
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    <identifier type="doi">10.18653/v1/2021.conll-1.41</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Negation-Instance Based Evaluation of End-to-End Negation Resolution
%A Sineva, Elizaveta
%A Grünewald, Stefan
%A Friedrich, Annemarie
%A Kuhn, Jonas
%Y Bisazza, Arianna
%Y Abend, Omri
%S Proceedings of the 25th Conference on Computational Natural Language Learning
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F sineva-etal-2021-negation
%X In this paper, we revisit the task of negation resolution, which includes the subtasks of cue detection (e.g. “not”, “never”) and scope resolution. In the context of previous shared tasks, a variety of evaluation metrics have been proposed. Subsequent works usually use different subsets of these, including variations and custom implementations, rendering meaningful comparisons between systems difficult. Examining the problem both from a linguistic perspective and from a downstream viewpoint, we here argue for a negation-instance based approach to evaluating negation resolution. Our proposed metrics correspond to expectations over per-instance scores and hence are intuitively interpretable. To render research comparable and to foster future work, we provide results for a set of current state-of-the-art systems for negation resolution on three English corpora, and make our implementation of the evaluation scripts publicly available.
%R 10.18653/v1/2021.conll-1.41
%U https://aclanthology.org/2021.conll-1.41
%U https://doi.org/10.18653/v1/2021.conll-1.41
%P 528-543

Markdown (Informal)

[Negation-Instance Based Evaluation of End-to-End Negation Resolution](https://aclanthology.org/2021.conll-1.41) (Sineva et al., CoNLL 2021)

• Negation-Instance Based Evaluation of End-to-End Negation Resolution (Sineva et al., CoNLL 2021)

ACL

• Elizaveta Sineva, Stefan Grünewald, Annemarie Friedrich, and Jonas Kuhn. 2021. Negation-Instance Based Evaluation of End-to-End Negation Resolution. In Proceedings of the 25th Conference on Computational Natural Language Learning, pages 528–543, Online. Association for Computational Linguistics.