Lexical Aspectual Classification

This work is a first attempt at classification of Lexical Aspect. In this dissertation I describe eight lexical aspectual classes, each initially containing a few members. Using distributional analysis I generate 132 additional seeds, each of which was approved by at least seven out of nine judges. These seeds are in turn fed into a supervised machine learning system, trained on 136 lexical and syntactic features. I experiment on one 8-way classification task, one 3-way classification task, and ten binary classification tasks, and show that five of the eight classes are identified better than by a random baseline measure by a statistically significant margin. Finally, I analyze the relative contribution of each of four feature groups and conclude that the same features which are best in identifying phrasal aspect are also most informative for lexical aspect.

Grantor dc:publisher

Université d'Ottawa / University of Ottawa

Year dc:date

2012

Author dc:creator

• Richard, Keelan

Contributors dc:contributor

• Szpakowicz, Stanislaw

Language dc:language

en

Identifier

http://dx.doi.org/10.20381/ruor-5835

OAI identifier oai:identifier

oai:ruor.uottawa.ca:10393/22906