Proceedings of the Workshop on Annotating and Reasoning about Time and Events - ARTE '06

Abstract

The computational analysis of time is a challenging and very topical problem, as the needs of applications based on information extraction techniques expand to include varying degrees of time stamping and temporal ordering of events and/or relations within a narrative. The challenges derive from the combined requirements of a mapping process (text to a rich representation of temporal entities), representational framework (ontologically-grounded temporal graph), and reasoning capability (combining commonsense inference with temporal axioms). Usually contextualized in question-answering applications (with obvious dependencies of answers on time), temporal awareness directly impacts numerous areas of NLP and AI: text summarization over events and their participants; making inferences from events in a text; overlaying timelines on document collections; commonsense reasoning in narrative and story understanding.