Year: 2019
Type: conference-paper
Abstract: Given the fact that verbs play a crucial role in language comprehension, this paper presents a study which compares the verb senses in English PropBank with the ones in English WordNet through manual tagging.After analyzing 1554 senses in 1453 distinct verbs, we have found out that while the majority of the senses in Prop-Bank have their one-to-one correspondents in WordNet, a substantial amount of them are differentiated.Furthermore, by analysing the differences between our manually-tagged and an automaticallytagged resource, we claim that manual tagging can help provide better results in sense annotation.
Source: Global WordNet Conference (GWC)
Authors: Özge Bakay, Begüm Avar, Olcay Taner Yıldız
Institutions: Boğaziçi University, Işık University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3158735793