Paper: Extracting and modeling durations for habits and events from Twitter

ACL ID P12-2044
Title Extracting and modeling durations for habits and events from Twitter
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

We seek to automatically estimate typical durations for events and habits described in Twitter tweets. A corpus of more than 14 million tweets containing temporal du- ration information was collected. These tweets were classified as to their habituality status using a bootstrapped, decision tree. For each verb lemma, associated duration information was collected for episodic and habitual uses of the verb. Summary statis- tics for 483 verb lemmas and their typical habit and episode durations has been com- piled and made available. This automati- cally generated duration information is broadly comparable to hand-annotation.