Automatic identification of aspectual classes across verbal readings

The automatic prediction of aspectual classes is very challenging for verbs whose aspectual value varies across readings, which are the rule rather than the exception. This paper sheds a new perspective on this problem by using a machine learning approach and a rich morpho-syntactic and semantic valency lexicon. In contrast to previous work, where the aspectual value of corpus clauses is determined on the basis of features retrieved from the corpus, we use features extracted from the lexicon, and aim to predict the aspectual value of verbal readings rather than verbs. Studying the performance of the classifiers on a set of manually annotated verbal readings, we found that our lexicon provided enough information to reliably predict the aspectual value of verbs across their readings. We additionally tested our predictions for unseen predicates through a task based evaluation, by using them in the automatic detection of temporal relation types in TempEval 2007 tasks for French. These experiments also confirmed the reliability of our aspectual predictions, even for unseen verbs.