  The interpretation of the lexical aspect of verbs in English plays a crucial
role for recognizing textual entailment and learning discourse-level
inferences. We show that two elementary dimensions of aspectual class, states
vs. events, and telic vs. atelic events, can be modelled effectively with
distributional semantics. We find that a verb's local context is most
indicative of its aspectual class, and demonstrate that closed class words tend
to be stronger discriminating contexts than content words. Our approach
outperforms previous work on three datasets. Lastly, we contribute a dataset of
human--human conversations annotated with lexical aspect and present
experiments that show the correlation of telicity with genre and discourse
goals.
