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NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter
Newcastle University.
The University of Melbourne.
Umeå University. (Database and Data Mining Group)ORCID iD: 0000-0001-8820-2405
Deakin University.
Show others and affiliations
2018 (English)In: Proceedings of The 12th International Workshop on Semantic Evaluation, New Orleans, Louisiana, USA: The Association for Computational Linguistics , 2018Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony detection in English tweets". We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features.  Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank at fifth in terms of the accuracy metric and the F1 metric. Our code is available at: https://github.com/NIHRIO/IronyDetectionInTwitter

Place, publisher, year, edition, pages
New Orleans, Louisiana, USA: The Association for Computational Linguistics , 2018.
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:umu:diva-147650DOI: 10.18653/v1/S18-1085ISBN: 978-1-948087-20-9 (print)OAI: oai:DiVA.org:umu-147650DiVA, id: diva2:1205271
Conference
the 12nd International Workshop on Semantic Evaluation (SemEval-2018)
Available from: 2018-05-12 Created: 2018-05-12 Last updated: 2019-06-18Bibliographically approved

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Vu, Xuan-Son

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf