Name
Abstract | ||
|---|---|---|
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 duration 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 statistics for 483 verb lemmas and their typical habit and episode durations has been compiled and made available. This automatically generated duration information is broadly comparable to hand-annotation. |
Year | Venue | Keywords |
|---|---|---|
2012 | ACL | episode duration,associated duration information,typical duration,typical habit,habitual use,verb lemma,twitter tweet,temporal duration information,decision tree,duration information |
Field | DocType | Volume |
Decision tree,Verb,Computer science,Bootstrapping,Habit,Natural language processing,Artificial intelligence,Summary statistics,Lemma (mathematics) | Conference | P12-2 |
Citations | PageRank | References |
4 | 0.40 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jennifer Williams | 1 | 35 | 11.67 |
| Graham Katz | 2 | 589 | 41.68 |