Unsupervised Mapping of Arguments of Deverbal Nouns to Their Corresponding Verbal Labels

Aviv Weinstein, Yoav Goldberg

Abstract
Deverbal nouns are nominal forms of verbs commonly used in written English texts to describe events or actions, as well as their arguments. However, many NLP systems, and in particular pattern-based ones, neglect to handle such nominalized constructions. The solutions that do exist for handling arguments of nominalized constructions are based on semantic annotation and require semantic ontologies, making their applications restricted to a small set of nouns. We propose to adopt instead a more syntactic approach, which maps the arguments of deverbal nouns to the universal-dependency relations of the corresponding verbal construction. We present an unsupervised mechanism—based on contextualized word representations—which allows to enrich universal-dependency trees with dependency arcs denoting arguments of deverbal nouns, using the same labels as the corresponding verbal cases. By sharing the same label set as in the verbal case, patterns that were developed for verbs can be applied without modification but with high accuracy also to the nominal constructions.
Anthology ID:
2023.findings-acl.184
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:
2921–2935
Language:
URL:
https://aclanthology.org/2023.findings-acl.184
DOI:
10.18653/v1/2023.findings-acl.184
Bibkey:
Cite (ACL):
Aviv Weinstein and Yoav Goldberg. 2023. Unsupervised Mapping of Arguments of Deverbal Nouns to Their Corresponding Verbal Labels. In Findings of the Association for Computational Linguistics: ACL 2023, pages 2921–2935, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Mapping of Arguments of Deverbal Nouns to Their Corresponding Verbal Labels (Weinstein & Goldberg, Findings 2023)
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