Temporal ontology and temporal reference
Computational Linguistics - Special issue on tense and aspect
Understanding the Yarowsky Algorithm
Computational Linguistics
NLTK: the natural language toolkit
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Using query patterns to learn the duration of events
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Learning Temporal Information for States and Events
ICSC '11 Proceedings of the 2011 IEEE Fifth International Conference on Semantic Computing
Annotating and learning event durations in text
Computational Linguistics
Extracting fine-grained durations for verbs from Twitter
ACL '12 Proceedings of ACL 2012 Student Research Workshop
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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.