Schulz, Claudia ; Meyer, Christian M. ; Kiesewetter, Jan ; Sailer, Michael ; Bauer, Elisabeth ; Fischer, Martin R. ; Fischer, Frank ; Gurevych, Iryna (2019)
Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains.
The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Florence, Italy (28.07.2019-02.08.2019)
doi: 10.18653/v1/P19-1265
Konferenzveröffentlichung, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Schulz, Claudia ; Meyer, Christian M. ; Kiesewetter, Jan ; Sailer, Michael ; Bauer, Elisabeth ; Fischer, Martin R. ; Fischer, Frank ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains |
| Sprache: | Englisch |
| Publikationsjahr: | 27 Mai 2019 |
| Ort: | Florence, Italy |
| Verlag: | Association for Computational Linguistics |
| Buchtitel: | Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics |
| Veranstaltungstitel: | The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) |
| Veranstaltungsort: | Florence, Italy |
| Veranstaltungsdatum: | 28.07.2019-02.08.2019 |
| DOI: | 10.18653/v1/P19-1265 |
| URL / URN: | https://aclanthology.org/P19-1265 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | 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. |
| Freie Schlagworte: | UKP_p_FAMULUS |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 18 Sep 2019 12:15 |
| Letzte Änderung: | 05 Jun 2024 08:12 |
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Verfügbare Versionen dieses Eintrags
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Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains. (deposited 27 Mai 2019 13:43)
- Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains. (deposited 18 Sep 2019 12:15) [Gegenwärtig angezeigt]
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