The Coreference under Transformation Labeling Dataset: Entity Tracking in Procedural Texts Using Event Models

Kyeongmin Rim, Jingxuan Tu, Bingyang Ye, Marc Verhagen, Eben Holderness, James Pustejovsky

Abstract
We demonstrate that coreference resolution in procedural texts is significantly improved when performing transformation-based entity linking prior to coreference relation identification. When events in the text introduce changes to the state of participating entities, it is often impossible to accurately link entities in anaphoric and coreference relations without an understanding of the transformations those entities undergo. We show how adding event semantics helps to better model entity coreference. We argue that all transformation predicates, not just creation verbs, introduce a new entity into the discourse, as a kind of generalized Result Role, which is typically not textually mentioned. This allows us to model procedural texts as process graphs and to compute the coreference type for any two entities in the recipe. We present our annotation methodology and the corpus generated as well as describe experiments on coreference resolution of entity mentions under a process-oriented model of events.
Anthology ID:
2023.findings-acl.788
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12448–12460
Language:
URL:
https://aclanthology.org/2023.findings-acl.788
DOI:
10.18653/v1/2023.findings-acl.788
Bibkey:
Cite (ACL):
Kyeongmin Rim, Jingxuan Tu, Bingyang Ye, Marc Verhagen, Eben Holderness, and James Pustejovsky. 2023. The Coreference under Transformation Labeling Dataset: Entity Tracking in Procedural Texts Using Event Models. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12448–12460, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
The Coreference under Transformation Labeling Dataset: Entity Tracking in Procedural Texts Using Event Models (Rim et al., Findings 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2023.findings-acl.788.pdf
PDF Search