Investigating statistical techniques for sentence-level event classification

Abstract

The ability to correctly classify sentences that describe events is an important task for many natural language applications such as Question Answering (QA) and Summarisation. In this paper, we treat event detection as a sentence level text classification problem. We compare the performance of two approaches to this task: a Support Vector Machine (SVM) classifier and a Language Modeling (LM) approach. We also investigate a rule based method that uses hand crafted lists of terms derived from WordNet. Lhese terms are strongly associated with a given event type, and can be used to identify sentences describing instances of that type. We use two datasets in our experiments, and evaluate each technique on six distinct event types. Our results indicate that the SVM consistently outperform the LM technique for this task. More interestingly, we discover that the manual rule based classification system is a very powerful baseline that outperforms the SVM on three of the six event types. © 2008. Licensed under the Creative Commons.