@inproceedings{joo-hwang-2019-steve,
title = "Steve {M}artin at {S}em{E}val-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan News",
author = "Joo, Youngjun and
Hwang, Inchon",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2171/",
doi = "10.18653/v1/S19-2171",
pages = "990--994",
abstract = "This paper describes our submission to task 4 in SemEval 2019, i.e., hyperpartisan news detection. Our model aims at detecting hyperpartisan news by incorporating the style-based features and the content-based features. We extract a broad number of feature sets and use as our learning algorithms the GBDT and the n-gram CNN model. Finally, we apply the weighted average for effective learning between the two models. Our model achieves an accuracy of 0.745 on the test set in subtask A."
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<abstract>This paper describes our submission to task 4 in SemEval 2019, i.e., hyperpartisan news detection. Our model aims at detecting hyperpartisan news by incorporating the style-based features and the content-based features. We extract a broad number of feature sets and use as our learning algorithms the GBDT and the n-gram CNN model. Finally, we apply the weighted average for effective learning between the two models. Our model achieves an accuracy of 0.745 on the test set in subtask A.</abstract>
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%0 Conference Proceedings
%T Steve Martin at SemEval-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan News
%A Joo, Youngjun
%A Hwang, Inchon
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F joo-hwang-2019-steve
%X This paper describes our submission to task 4 in SemEval 2019, i.e., hyperpartisan news detection. Our model aims at detecting hyperpartisan news by incorporating the style-based features and the content-based features. We extract a broad number of feature sets and use as our learning algorithms the GBDT and the n-gram CNN model. Finally, we apply the weighted average for effective learning between the two models. Our model achieves an accuracy of 0.745 on the test set in subtask A.
%R 10.18653/v1/S19-2171
%U https://aclanthology.org/S19-2171/
%U https://doi.org/10.18653/v1/S19-2171
%P 990-994
Markdown (Informal)
[Steve Martin at SemEval-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan News](https://aclanthology.org/S19-2171/) (Joo & Hwang, SemEval 2019)
ACL