Year: 2014
Type: conference-paper
Abstract: We present a supervised method for verb sense disambiguation based on VerbNet.Most previous supervised approaches to verb sense disambiguation create a classifier for each verb that reaches a frequency threshold.These methods, however, have a significant practical problem that they cannot be applied to rare or unseen verbs.In order to overcome this problem, we create a single classifier to be applied to rare or unseen verbs in a new text.This single classifier also exploits generalized semantic features of a verb and its modifiers in order to better deal with rare or unseen verbs.Our experimental results show that the proposed method achieves equivalent performance to per-verb classifiers, which cannot be applied to unseen verbs.Our classifier could be utilized to improve the classifications in lexical resources of verbs, such as VerbNet, in a semi-automatic manner and to possibly extend the coverage of these resources to new verbs.
Authors: Daisuke Kawahara, Martha Palmer
Institutions: Kyoto University, University of Colorado Boulder
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2251544378