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Understanding temporal and causal relations between events is a fundamentalnatural language understanding task. Because a cause must be before its effectin time, temporal and causal relations are closely related and one relationeven dictates the other one in many cases. However, limited attention has beenpaid to studying these two relations jointly. This paper presents a jointinference framework for them using constrained conditional models (CCMs).Specifically, we formulate the joint problem as an integer linear programming(ILP) problem, enforcing constraints inherently in the nature of time andcausality. We show that the joint inference framework results in statisticallysignificant improvement in the extraction of both temporal and causal relationsfrom text.

Joint Reasoning for Temporal and Causal Relations