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Determining Event Durations: Models and Error Analysis

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Description

This paper presents models to predict event durations.

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5 p.

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Vempala, Alakananda; Blanco, Eduardo & Palmer, Alexis June 1, 2018.

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This paper is part of the collection entitled: UNT Scholarly Works and was provided by the UNT College of Information to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 96 times. More information about this paper can be viewed below.

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Description

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.

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  • North American Chapter of the Association for Computational Linguistics (NAACL) - Human Language Technologies (HLT) Conference, 2018, New Orleans, Louisiana, United States

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  • 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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UNT Scholarly Works

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  • June 1, 2018

Added to The UNT Digital Library

  • June 15, 2018, 10:41 p.m.

Description Last Updated

  • Feb. 2, 2021, 2:57 p.m.

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Total Uses: 96

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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 17, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Information.

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