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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, Faculty of Science and Technology, Department of Computing Science. (Database and Data Mining Group)ORCID iD: 0000-0001-8820-2405
Deakin University.
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2018 (English)Conference 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, 2018.
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:umu:diva-147650OAI: oai:DiVA.org:umu-147650DiVA, id: diva2:1205271
Conference
Proceedings of the 12nd International Workshop on Semantic Evaluation (SemEval-2018)
Available from: 2018-05-12 Created: 2018-05-12 Last updated: 2018-06-09

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Vu, Xuan-Son

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