Can Selectional Preferences Help Automatic Semantic Role Labeling?
Year: 2015
Type: conference-paper
Abstract: We describe a topic model based approach for selectional preference. Using the topic features generated by an LDA model on the extracted predicate-arguments over the Chinese Gigaword corpus, we show improvement to our state-of-the-art Chinese SRL system by 2.34 F1 points on arguments of nominal predicates, 0.40 F1 point on arguments of verb predicates, and 0.66 F1 point overall. More over, similar gains were achieved on out-ofgenre test data, as well as on English SRL using the same technique.
DOI: 10.18653/v1/s15-1027
Authors: Shumin Wu, Martha Palmer
Institution: University of Colorado Boulder
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2110221394