@inproceedings{sherif-etal-2018-ctsys,
title = "{CTS}ys at {S}em{E}val-2018 Task 3: Irony in Tweets",
author = "Sherif, Myan and
Mamdouh, Sherine and
Ghazi, Wegdan",
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/S18-1094",
doi = "10.18653/v1/S18-1094",
pages = "576--580",
abstract = "The objective of this paper is to provide a description for a system built as our participation in SemEval-2018 Task 3 on Irony detection in English tweets. This system classifies a tweet as either ironic or non-ironic through a supervised learning approach. Our approach is to implement three feature models, and to then improve the performance of the supervised learning classification of tweets by combining many data features and using a voting system on four different classifiers. We describe the process of pre-processing data, extracting features, and running different types of classifiers against our feature set. In the competition, our system achieved an F1-score of 0.4675, ranking 35th in subtask A, and an F1-score score of 0.3014 ranking 22th in subtask B.",
}
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<abstract>The objective of this paper is to provide a description for a system built as our participation in SemEval-2018 Task 3 on Irony detection in English tweets. This system classifies a tweet as either ironic or non-ironic through a supervised learning approach. Our approach is to implement three feature models, and to then improve the performance of the supervised learning classification of tweets by combining many data features and using a voting system on four different classifiers. We describe the process of pre-processing data, extracting features, and running different types of classifiers against our feature set. In the competition, our system achieved an F1-score of 0.4675, ranking 35th in subtask A, and an F1-score score of 0.3014 ranking 22th in subtask B.</abstract>
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%0 Conference Proceedings
%T CTSys at SemEval-2018 Task 3: Irony in Tweets
%A Sherif, Myan
%A Mamdouh, Sherine
%A Ghazi, Wegdan
%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 sherif-etal-2018-ctsys
%X The objective of this paper is to provide a description for a system built as our participation in SemEval-2018 Task 3 on Irony detection in English tweets. This system classifies a tweet as either ironic or non-ironic through a supervised learning approach. Our approach is to implement three feature models, and to then improve the performance of the supervised learning classification of tweets by combining many data features and using a voting system on four different classifiers. We describe the process of pre-processing data, extracting features, and running different types of classifiers against our feature set. In the competition, our system achieved an F1-score of 0.4675, ranking 35th in subtask A, and an F1-score score of 0.3014 ranking 22th in subtask B.
%R 10.18653/v1/S18-1094
%U https://aclanthology.org/S18-1094
%U https://doi.org/10.18653/v1/S18-1094
%P 576-580
Markdown (Informal)
[CTSys at SemEval-2018 Task 3: Irony in Tweets](https://aclanthology.org/S18-1094) (Sherif et al., SemEval 2018)
ACL
- Myan Sherif, Sherine Mamdouh, and Wegdan Ghazi. 2018. CTSys at SemEval-2018 Task 3: Irony in Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 576–580, New Orleans, Louisiana. Association for Computational Linguistics.