@inproceedings{cruz-etal-2019-team,
title = "Team Fernando-Pessa at {S}em{E}val-2019 Task 4: Back to Basics in Hyperpartisan News Detection",
author = "Cruz, Andr{\'e} and
Rocha, Gil and
Sousa-Silva, Rui and
Lopes Cardoso, Henrique",
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/S19-2173",
doi = "10.18653/v1/S19-2173",
pages = "999--1003",
abstract = "This paper describes our submission to the SemEval 2019 Hyperpartisan News Detection task. Our system aims for a linguistics-based document classification from a minimal set of interpretable features, while maintaining good performance. To this goal, we follow a feature-based approach and perform several experiments with different machine learning classifiers. Additionally, we explore feature importances and distributions among the two classes. On the main task, our model achieved an accuracy of 71.7{\%}, which was improved after the task{'}s end to 72.9{\%}. We also participate on the meta-learning sub-task, for classifying documents with the binary classifications of all submitted systems as input, achieving an accuracy of 89.9{\%}.",
}
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%0 Conference Proceedings
%T Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection
%A Cruz, André
%A Rocha, Gil
%A Sousa-Silva, Rui
%A Lopes Cardoso, Henrique
%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 cruz-etal-2019-team
%X This paper describes our submission to the SemEval 2019 Hyperpartisan News Detection task. Our system aims for a linguistics-based document classification from a minimal set of interpretable features, while maintaining good performance. To this goal, we follow a feature-based approach and perform several experiments with different machine learning classifiers. Additionally, we explore feature importances and distributions among the two classes. On the main task, our model achieved an accuracy of 71.7%, which was improved after the task’s end to 72.9%. We also participate on the meta-learning sub-task, for classifying documents with the binary classifications of all submitted systems as input, achieving an accuracy of 89.9%.
%R 10.18653/v1/S19-2173
%U https://aclanthology.org/S19-2173
%U https://doi.org/10.18653/v1/S19-2173
%P 999-1003
Markdown (Informal)
[Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection](https://aclanthology.org/S19-2173) (Cruz et al., SemEval 2019)
ACL