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Analysis of automatic annotation suggestions for hard discourse-level tasks in expert domains

  • Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation. To speed up and ease annotations, we investigate the viability of automatically generated annotation suggestions for such tasks. As an example, we choose a task that is particularly hard for both humans and machines: the segmentation and classification of epistemic activities in diagnostic reasoning texts. We create and publish a new dataset covering two domains and carefully analyse the suggested annotations. We find that suggestions have positive effects on annotation speed and performance, while not introducing noteworthy biases. Envisioning suggestion models that improve with newly annotated texts, we contrast methods for continuous model adjustment and suggest the most effective setup for suggestions in future expert tasks.

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Metadaten
Author:Claudia Schulz, Christian M. Meyer, Jan Kiesewetter, Michael SailerORCiDGND, Elisabeth BauerORCiDGND, Martin R. Fischer, Frank Fischer, Iryna Gurevych
URN:urn:nbn:de:bvb:384-opus4-1116723
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/111672
ISBN:978-1-950737-48-2OPAC
Parent Title (English):Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, July 28 - August, 2019, Florence, Italy
Publisher:Association for Computational Linguistics (ACL)
Place of publication:Stroudsburg, PA
Editor:Anna Korhonen, David Traum, Lluís Màrquez
Type:Conference Proceeding
Language:English
Date of Publication (online):2024/02/28
Year of first Publication:2019
Publishing Institution:Universität Augsburg
Release Date:2024/02/28
First Page:2761
Last Page:2772
DOI:https://doi.org/10.18653/v1/P19-1265
Institutes:Philosophisch-Sozialwissenschaftliche Fakultät
Philosophisch-Sozialwissenschaftliche Fakultät / Empirische Bildungsforschung
Philosophisch-Sozialwissenschaftliche Fakultät / Empirische Bildungsforschung / Lehrstuhl für Learning Analytics and Educational Data Mining
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung

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