NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter

Thanh Vu; Dat Quoc Nguyen; Xuan-Son Vu; Dai Quoc Nguyen; Michael Catt; Michael Trenell

doi:10.18653/v1/S18-1085

NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter

Thanh Vu, Dat Quoc Nguyen, Xuan-Son Vu, Dai Quoc Nguyen, Michael Catt, Michael Trenell

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Abstract

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 least fourth using the accuracy metric and sixth using the F1 metric. Our code is available at: https://github.com/NIHRIO/IronyDetectionInTwitter

Anthology ID:: S18-1085
Volume:: Proceedings of the 12th International Workshop on Semantic Evaluation
Month:: June
Year:: 2018
Address:: New Orleans, Louisiana
Editors:: Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:: SemEval
SIG:: SIGLEX
Publisher:: Association for Computational Linguistics
Note:
Pages:: 525–530
Language:
URL:: https://aclanthology.org/S18-1085/
DOI:: 10.18653/v1/S18-1085
Bibkey:
Cite (ACL):: Thanh Vu, Dat Quoc Nguyen, Xuan-Son Vu, Dai Quoc Nguyen, Michael Catt, and Michael Trenell. 2018. NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 525–530, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):: NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter (Vu et al., SemEval 2018)
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PDF:: https://aclanthology.org/S18-1085.pdf

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@inproceedings{vu-etal-2018-nihrio,
    title = "{NIHRIO} at {S}em{E}val-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in {T}witter",
    author = "Vu, Thanh  and
      Nguyen, Dat Quoc  and
      Vu, Xuan-Son  and
      Nguyen, Dai Quoc  and
      Catt, Michael  and
      Trenell, Michael",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S18-1085/",
    doi = "10.18653/v1/S18-1085",
    pages = "525--530",
    abstract = "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 least fourth using the accuracy metric and sixth using the F1 metric. Our code is available at: \url{https://github.com/NIHRIO/IronyDetectionInTwitter}"
}

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%0 Conference Proceedings
%T NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter
%A Vu, Thanh
%A Nguyen, Dat Quoc
%A Vu, Xuan-Son
%A Nguyen, Dai Quoc
%A Catt, Michael
%A Trenell, Michael
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F vu-etal-2018-nihrio
%X 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 least fourth using the accuracy metric and sixth using the F1 metric. Our code is available at: https://github.com/NIHRIO/IronyDetectionInTwitter
%R 10.18653/v1/S18-1085
%U https://aclanthology.org/S18-1085/
%U https://doi.org/10.18653/v1/S18-1085
%P 525-530

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Markdown (Informal)

[NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter](https://aclanthology.org/S18-1085/) (Vu et al., SemEval 2018)

NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter (Vu et al., SemEval 2018)

ACL

Thanh Vu, Dat Quoc Nguyen, Xuan-Son Vu, Dai Quoc Nguyen, Michael Catt, and Michael Trenell. 2018. NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 525–530, New Orleans, Louisiana. Association for Computational Linguistics.