Temporal ontology and temporal reference
Computational Linguistics - Special issue on tense and aspect
NLTK: the natural language toolkit
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Journal of the American Society for Information Science and Technology
Using query patterns to learn the duration of events
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Identifying sarcasm in Twitter: a closer look
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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 and modeling durations for habits and events from Twitter
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
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This paper presents recent work on a new method to automatically extract finegrained duration information for common verbs using a large corpus of Twitter tweets. Regular expressions were used to extract verbs and durations from each tweet in a corpus of more than 14 million tweets with 90.38% precision covering 486 verb lemmas. Descriptive statistics for each verb lemma were found as well as the most typical fine-grained duration measure. Mean durations were compared with previous work by Gusev et al. (2011) and it was found that there is a small positive correlation.