Temporal Relation Classification using a Model of Tense and Aspect.

Determining the temporal order of events in a text is difficult. However, it is crucial to the extraction of narratives, plans, and context. We suggest that a simple, established framework of tense and aspect provides a viable model for ordering a subset of events and times in a given text. Using this framework, we investigate extracting features that represent temporal information and integrate these in a machine learning approach. These features improve event-event ordering.