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Previous work on ordering events in text has typically focused on local pairwise decisions, ignoring globally inconsistent labels. However, temporal ordering is the type of domain in which global constraints should be relatively easy to represent and reason over. This paper presents a framework that informs local decisions with two types of implicit global constraints: transitivity (A before B and B before C implies A before C) and time expression normalization (e.g. last month is before yesterday). We show how these constraints can be used to create a more densely-connected network of events, and how global consistency can be enforced by incorporating these constraints into an integer linear programming framework. We present results on two event ordering tasks, showing a 3.6% absolute increase in the accuracy of before/after classification over a pairwise model.
Chambers et al. (Tue,) studied this question.