Using inference for evaluating models of temporal discourse Philippe Muller and Axel Reymoneti   View Paper (PDF)   Return to Temporal Representation and Reasoning in AI 2 Abstract This paper addresses the problem of building and evaluating models of the temporal interpretation of a discourse in Natural Language. The extraction of temporal information is a complicated task as it is not limited to finding pieces of information at specific places in a text. A lot of temporal data is made of relations between events, or relations between events and dates. Building such information is highly context-dependent, taking into account information more than a sentence at a time. Moreover it is not clear what the target representation should be: the way it is done by human beings is still a subject of study in itself. It seems to require some sort of reasoning, either purely temporal or involving complex world knowledge. This is the reason why evaluating this task is also problematic when trying to design a system for it. We present a method for enriching the detection of event-to-event relations with a basic reasoning model, that can be also used for helping to compare the extraction of temporal information by a system and by a human being. We have experimented with this method on a set of texts, comparing a very basic model of tense interpretation with a more complex model inspired by the Reichenbach's well-known theory of narrative discourse. ©2006 Association for Computing Machinery