Verbose Labels for Semantic Roles

Date created: 
2013-01-16
Identifier: 
etd7632
Keywords: 
Semantic Role Labeling
Verbose Labeling
Text Visualization
Verb Sense Prediction
PropBank
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 type: 
Thesis
Rights: 
Copyright remains with the author. The author granted permission for the file to be printed and for the text to be copied and pasted.
File(s): 
Senior supervisor: 
Anoop Sarkar
Fred Popowich
Department: 
Applied Sciences: School of Computing Science
Thesis type: 
(Thesis) M.Sc.
Statistics: