Paper: Combining Probability-Based Rankers for Action-Item Detection

ACL ID N07-1041
Title Combining Probability-Based Rankers for Action-Item Detection
Venue Human Language Technologies
Session Main Conference
Year 2007

This paper studies methods that automat- ically detect action-items in e-mail, an important category for assisting users in identifying new tasks, tracking ongoing ones, and searching for completed ones. Since action-items consist of a short span of text, classifiers that detect action-items can be built from a document-level or a sentence-level view. Rather than com- mit to either view, we adapt a context- sensitive metaclassification framework to this problem to combine the rankings pro- duced by different algorithms as well as different views. While this framework is known to work well for standard classi- fication, its suitability for fusing rankers has not been studied. In an empirical eval- uation, the resulting approach yields im- proved rankings that are less sensitive to training ...