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

TL;DR

This paper introduces an unsupervised, syntactic method using contextualized word representations to map arguments of deverbal nouns to their verbal labels, enhancing NLP systems' handling of nominalized constructions without relying on semantic ontologies.

Contribution

It proposes a novel unsupervised approach that maps deverbal noun arguments to verbal labels using contextualized embeddings, avoiding the need for semantic ontologies.

Findings

Enriches universal-dependency trees with argument arcs for deverbal nouns

Achieves high accuracy in argument mapping without semantic ontologies

Allows verb-based patterns to be applied to nominalized constructions

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.