Tintin at SemEval-2019 Task 4: Detecting Hyperpartisan News Article with only Simple Tokens

Yves Bestgen


Abstract
Tintin, the system proposed by the CECL for the Hyperpartisan News Detection task of SemEval 2019, is exclusively based on the tokens that make up the documents and a standard supervised learning procedure. It obtained very contrasting results: poor on the main task, but much more effective at distinguishing documents published by hyperpartisan media outlets from unbiased ones, as it ranked first. An analysis of the most important features highlighted the positive aspects, but also some potential limitations of the approach.
Anthology ID:
S19-2186
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:
1062–1066
Language:
URL:
https://aclanthology.org/S19-2186
DOI:
10.18653/v1/S19-2186
Bibkey:
Cite (ACL):
Yves Bestgen. 2019. Tintin at SemEval-2019 Task 4: Detecting Hyperpartisan News Article with only Simple Tokens. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1062–1066, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Tintin at SemEval-2019 Task 4: Detecting Hyperpartisan News Article with only Simple Tokens (Bestgen, SemEval 2019)
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PDF:
https://aclanthology.org/S19-2186.pdf