Skip to main content

Verbose Labels for Semantic Roles

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2013-01-16
Authors/Contributors
Abstract
We introduce a new task that takes the output of semantic role labeling and associates each of the argument slots for a predicate with a verbose description such as buyer or thing_bought to semantic role labels such as `Arg0' and `Arg1' for predicate like "buy". Ambiguous verb senses and syntactic alternations make this a challenging task. We adapt the frame information for each verb in the PropBank to create our training data. We propose various baseline methods and more informed models which can identify such verbose labels with 95.2% accuracy if the semantic roles have already been correctly identified. We extend our work to text visualization to illustrate the importance of verbose labeling. As a proof of concept, we built an interactive browser for human history articles from Wikipedia, called lensingWikipedia.
Document
Identifier
etd7632
Copyright statement
Copyright is held by the author.
Permissions
The author granted permission for the file to be printed and for the text to be copied and pasted.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Sarkar, Anoop
Thesis advisor: Popowich, Fred
Member of collection
Download file Size
etd7632_RVadlapudi.pdf 1.83 MB

Views & downloads - as of June 2023

Views: 0
Downloads: 0