  This paper tackles temporal resolution of documents, such as determining when
a document is about or when it was written, based only on its text. We apply
techniques from information retrieval that predict dates via language models
over a discretized timeline. Unlike most previous works, we rely {\it solely}
on temporal cues implicit in the text. We consider both document-likelihood and
divergence based techniques and several smoothing methods for both of them. Our
best model predicts the mid-point of individuals' lives with a median of 22 and
mean error of 36 years for Wikipedia biographies from 3800 B.C. to the present
day. We also show that this approach works well when training on such
biographies and predicting dates both for non-biographical Wikipedia pages
about specific years (500 B.C. to 2010 A.D.) and for publication dates of short
stories (1798 to 2008). Together, our work shows that, even in absence of
temporal extraction resources, it is possible to achieve remarkable temporal
locality across a diverse set of texts.
