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

Aviv Weinstein, Yoav Goldberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics, ACL 2023
PublisherAssociation for Computational Linguistics (ACL)
Pages2921-2935
Number of pages15
ISBN (Electronic)9781959429623
StatePublished - 2023
Event61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada
Duration: 9 Jul 202314 Jul 2023

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
Country/TerritoryCanada
CityToronto
Period9/07/2314/07/23

Bibliographical note

Publisher Copyright:
© 2023 Association for Computational Linguistics.

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