Determining Event Durations: Models and Error Analysis

Alakananda Vempala, Eduardo Blanco, Alexis Palmer


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.
Anthology ID:
N18-2026
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
164–168
Language:
URL:
https://www.aclweb.org/anthology/N18-2026
DOI:
10.18653/v1/N18-2026
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-2026.pdf