Abstract
This paper presents models to predict event durations. We introduce aspectual features that capture deeper linguistic information than previous work, and experiment with neural networks. Our analysis shows that tense, aspect and temporal structure of the clause provide useful clues, and that an LSTM ensemble captures relevant context around the event.
Cite
CITATION STYLE
Vempala, A., Blanco, E., & Palmer, A. (2018). Determining event durations: Models and error analysis. In NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference (Vol. 2, pp. 164–168). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/n18-2026
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