Abstract
We have constructed a corpus of news articles in which events are annotated for estimated bounds on their duration. Here we describe a method for measuring interannotator agreement for these event duration distributions. We then show that machine learning techniques applied to this data yield coarse-grained event duration information, considerably outperforming a baseline and approaching human performance. © 2006 Association for Computational Linguistics.
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CITATION STYLE
Pan, F., Mulkar, R., & Hobbs, J. R. (2006). Learning event durations from event descriptions. In COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Vol. 1, pp. 393–400). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1220175.1220225
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