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
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 164–168
- Language:
- URL:
- https://aclanthology.org/N18-2026
- DOI:
- 10.18653/v1/N18-2026
- Cite (ACL):
- Alakananda Vempala, Eduardo Blanco, and Alexis Palmer. 2018. Determining Event Durations: Models and Error Analysis. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 164–168, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Determining Event Durations: Models and Error Analysis (Vempala et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/N18-2026.pdf