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This paper presents models to predict event durations.
Physical Description
5 p.
Notes
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.
North American Chapter of the Association for Computational Linguistics (NAACL) - Human Language Technologies (HLT) Conference, 2018, New Orleans, Louisiana, United States
Publication Title:
Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL) - Human Language Technologies (HLT) Conference, 2018, New Orleans, Louisiana, United States
Pages:
5
Page Start:
164
Page End:
168
Peer Reviewed:
Yes
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Vempala, Alakananda; Blanco, Eduardo & Palmer, Alexis.Determining Event Durations: Models and Error Analysis,
paper,
June 1, 2018;
Stroudsburg, Pennsylvania.
(https://digital.library.unt.edu/ark:/67531/metadc1164522/:
accessed April 18, 2025),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT College of Information.
