@inproceedings{swanberg-etal-2018-alanis,
title = "{ALANIS} at {S}em{E}val-2018 Task 3: A Feature Engineering Approach to Irony Detection in {E}nglish Tweets",
author = "Swanberg, Kevin and
Mirza, Madiha and
Pedersen, Ted and
Wang, Zhenduo",
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/S18-1082",
doi = "10.18653/v1/S18-1082",
pages = "507--511",
abstract = "This paper describes the ALANIS system that participated in Task 3 of SemEval-2018. We develop a system for detection of irony, as well as the detection of three types of irony: verbal polar irony, other verbal irony, and situational irony. The system uses a logistic regression model in subtask A and a voted classifier system with manually developed features to identify ironic tweets. This model improves on a naive bayes baseline by about 8 percent on training set.",
}
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%0 Conference Proceedings
%T ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets
%A Swanberg, Kevin
%A Mirza, Madiha
%A Pedersen, Ted
%A Wang, Zhenduo
%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 swanberg-etal-2018-alanis
%X This paper describes the ALANIS system that participated in Task 3 of SemEval-2018. We develop a system for detection of irony, as well as the detection of three types of irony: verbal polar irony, other verbal irony, and situational irony. The system uses a logistic regression model in subtask A and a voted classifier system with manually developed features to identify ironic tweets. This model improves on a naive bayes baseline by about 8 percent on training set.
%R 10.18653/v1/S18-1082
%U https://aclanthology.org/S18-1082
%U https://doi.org/10.18653/v1/S18-1082
%P 507-511
Markdown (Informal)
[ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets](https://aclanthology.org/S18-1082) (Swanberg et al., SemEval 2018)
ACL