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Carbonell J: Combining Probability-Based Rankers for ActionItem Detection

By Paul N. Bennett


This paper studies methods that automatically 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 commit to either view, we adapt a contextsensitive metaclassification framework to this problem to combine the rankings produced by different algorithms as well as different views. While this framework is known to work well for standard classification, its suitability for fusing rankers has not been studied. In an empirical evaluation, the resulting approach yields improved rankings that are less sensitive to training set variation, and furthermore, the theoretically-motivated reliability indicators we introduce enable the metaclassifier to now be applicable in any problem where the base classifiers are used.

Year: 2008
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