Year: 2023
Type: conference-paper
Abstract: Verbs like make, have and get present challenges for applications requiring automatic word sense discrimination.These verbs are both highly frequent and polysemous, with semantically "full" readings, as in make dinner, and "light" readings, as in make a request.Lexical resources like WordNet encode dozens of senses, making discrimination difficult and inviting proposals for reducing the number of entries or grouping them into coarser-grained supersenses.We propose a data-driven, linguistically-based approach to establishing a motivated sense inventory, focusing on make to establish a proof of concept.
Source: Global WordNet Conference (GWC)
Authors: Julie Kallini, Christiane Fellbaum
Institution: Princeton University
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7103890477
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