TweeTime: A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in Twitter
This addresses the challenge of temporal analysis in social media for researchers and applications, offering a domain-specific improvement.
The paper tackles the problem of recognizing and normalizing time expressions in Twitter text, where existing methods designed for well-edited text suffer from domain mismatch, and achieves a 0.68 F1 score on end-to-end date resolution, outperforming state-of-the-art systems.
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.