Temporal reasoners for document understand- ing typically assume that a document?s cre- ation date is known. Algorithms to ground relative time expressions and order events of- ten rely on this timestamp to assist the learner. Unfortunately, the timestamp is not always known, particularly on the Web. This pa- per addresses the task of automatic document timestamping, presenting two new models that incorporate rich linguistic features about time. The first is a discriminative classifier with new features extracted from the text?s time expressions (e.g., ?since 1999?). This model alone improves on previous generative mod- els by 77%. The second model learns prob- abilistic constraints between time expressions and the unknown document time. Imposing these learned constraints on the discrimina...

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