Determining event durations: Models and error analysis

Alakananda Vempala, Eduardo Blanco, Alexis Palmer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationShort Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages164-168
Number of pages5
ISBN (Electronic)9781948087292
StatePublished - 2018
Externally publishedYes
Event2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States
Duration: Jun 1 2018Jun 6 2018

Publication series

NameNAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Volume2

Conference

Conference2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
Country/TerritoryUnited States
CityNew Orleans
Period6/1/186/6/18

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics
  • Computer Science Applications

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