Academic Commons

Articles

Learning Methods to Combine Linguistic Indicators: Improving Aspectual Classification and Revealing Linguistic Insights

McKeown, Kathleen; Siegel, Eric V.

Aspectual classification maps verbs to a small set of primitive categories in order to reason about time. This classification is necessary for interpreting temporal modifiers and assessing temporal relationships, and is therefore a required component for many natural language applications.A verb's aspectual category can be predicted by co-occurrence frequencies between the verb and certain linguistic modifiers. These frequency measures, called linguistic indicators, are chosen by linguistic insights. However, linguistic indicators used in isolation are predictively incomplete, and are therefore insufficient when used individually.In this article, we compare three supervised machine learning methods for combining multiple linguistic indicators for aspectual classification: decision trees, genetic programming, and logistic regression. A set of 14 indicators are combined for classification according to two aspectual distinctions. This approach improves the classification performance for both distinctions, as evaluated over unrestricted sets of verbs occurring across two corpora. This demonstrates the effectiveness of the linguistic indicators and provides a much-needed full-scale method for automatic aspectual classification. Moreover, the models resulting from learning reveal several linguistic insights that are relevant to aspectual classification. We also compare supervised learning methods with an unsupervised method for this task.

Subjects

Files

  • thumnail for J00-4004Learning_Methods_to_Combine_Linguistic.pdf J00-4004Learning_Methods_to_Combine_Linguistic.pdf application/pdf 1.87 MB Download File

Also Published In

Title
Computational Linguistics
DOI
https://doi.org/10.1162/089120100750105957

More About This Work

Academic Units
Computer Science
Published Here
April 8, 2013