Aspectual Type and Temporal Relation Classification

In this paper we investigate the relevance of aspectual type for the problem of temporal information processing, i.e. the problems of the recent TempEval challenges. For a large list of verbs, we obtain several indicators about their lexical aspect by querying the web for expressions where these verbs occur in contexts associated with specific aspectual types. We then proceed to extend existing solutions for the problem of temporal information processing with the information extracted this way. The improved performance of the resulting models shows that (i) aspectual type can be data-mined with unsupervised methods with a level of noise that does not prevent this information from being useful and that (ii) temporal information processing can profit from information about aspectual type.

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