@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
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)
ACL