Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection

André Cruz, Gil Rocha, Rui Sousa-Silva, Henrique Lopes Cardoso

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%.
Anthology ID:
S19-2173
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
999–1003
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/S19-2173/
DOI:
10.18653/v1/S19-2173
Bibkey:
Cite (ACL):
André Cruz, Gil Rocha, Rui Sousa-Silva, and Henrique Lopes Cardoso. 2019. Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 999–1003, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection (Cruz et al., SemEval 2019)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/S19-2173.pdf
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