  We describe TweeTIME, a temporal tagger for recognizing and normalizing time
expressions in Twitter. Most previous work in social media analysis has to rely
on temporal resolvers that are designed for well-edited text, and therefore
suffer from the reduced performance due to domain mismatch. We present a
minimally supervised method that learns from large quantities of unlabeled data
and requires no hand-engineered rules or hand-annotated training corpora.
TweeTIME achieves 0.68 F1 score on the end-to-end task of resolving date
expressions, outperforming a broad range of state-of-the-art systems.
