@inproceedings{karasakalidis-etal-2021-duth,
title = "{DUTH} at {S}em{E}val-2021 Task 7: Is Conventional Machine Learning for Humorous and Offensive Tasks enough in 2021?",
author = "Karasakalidis, Alexandros and
Effrosynidis, Dimitrios and
Arampatzis, Avi",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.157",
doi = "10.18653/v1/2021.semeval-1.157",
pages = "1125--1129",
abstract = "This paper describes the approach that was developed for SemEval 2021 Task 7 (Hahackathon: Incorporating Demographic Factors into Shared Humor Tasks) by the DUTH Team. We used and compared a variety of preprocessing techniques, vectorization methods, and numerous conventional machine learning algorithms, in order to construct classification and regression models for the given tasks. We used majority voting to combine the models{'} outputs with small Neural Networks (NN) for classification tasks and their mean for regression for improving our system{'}s performance. While these methods proved weaker than modern, deep learning models, they are still relevant in research tasks because of their low requirements on computational power and faster training.",
}
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<abstract>This paper describes the approach that was developed for SemEval 2021 Task 7 (Hahackathon: Incorporating Demographic Factors into Shared Humor Tasks) by the DUTH Team. We used and compared a variety of preprocessing techniques, vectorization methods, and numerous conventional machine learning algorithms, in order to construct classification and regression models for the given tasks. We used majority voting to combine the models’ outputs with small Neural Networks (NN) for classification tasks and their mean for regression for improving our system’s performance. While these methods proved weaker than modern, deep learning models, they are still relevant in research tasks because of their low requirements on computational power and faster training.</abstract>
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%0 Conference Proceedings
%T DUTH at SemEval-2021 Task 7: Is Conventional Machine Learning for Humorous and Offensive Tasks enough in 2021?
%A Karasakalidis, Alexandros
%A Effrosynidis, Dimitrios
%A Arampatzis, Avi
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F karasakalidis-etal-2021-duth
%X This paper describes the approach that was developed for SemEval 2021 Task 7 (Hahackathon: Incorporating Demographic Factors into Shared Humor Tasks) by the DUTH Team. We used and compared a variety of preprocessing techniques, vectorization methods, and numerous conventional machine learning algorithms, in order to construct classification and regression models for the given tasks. We used majority voting to combine the models’ outputs with small Neural Networks (NN) for classification tasks and their mean for regression for improving our system’s performance. While these methods proved weaker than modern, deep learning models, they are still relevant in research tasks because of their low requirements on computational power and faster training.
%R 10.18653/v1/2021.semeval-1.157
%U https://aclanthology.org/2021.semeval-1.157
%U https://doi.org/10.18653/v1/2021.semeval-1.157
%P 1125-1129
Markdown (Informal)
[DUTH at SemEval-2021 Task 7: Is Conventional Machine Learning for Humorous and Offensive Tasks enough in 2021?](https://aclanthology.org/2021.semeval-1.157) (Karasakalidis et al., SemEval 2021)
ACL