A widely recognized problem in temporal annotation, is that annotators regularly miss temporal cues in the text, especially when annotating TLinks between pairs of events, as there are so many possible relations to annotate, and not all event-pairs are equally useful to annotate.

A frequently made TR-based preprocessing step is to calculate the transitive closure over the annotated relations, to obtain a more dense graph. The effect of temporal closure on performance for models that use no TR during prediction is ambivalent: it could improve or decrease improvement. When using a model that exploits TR also during prediction, data expansion through a temporal closure can be beneficial.