Negation-instance based evaluation of end-to-end negation resolution

  • 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.

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Author:Elizaveta Sineva, Stefan Grünewald, Annemarie FriedrichORCiDGND, Jonas Kuhn
Frontdoor URL
Parent Title (English):Proceedings of the 25th Conference on Computational Natural Language Learning, November 10–11, 2021, online
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, PA
Editor:Arianna Bisazza, Omri Abend
Type:Conference Proceeding
Year of first Publication:2021
Publishing Institution:Universität Augsburg
Release Date:2023/07/10
First Page:528
Last Page:543
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Professur für Sprachverstehen mit der Anwendung Digital Humanities
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)