Team Peter-Parker at SemEval-2019 Task 4: BERT-Based Method in Hyperpartisan News Detection

Zhiyuan Ning, Yuanzhen Lin, Ruichao Zhong

Abstract
This paper describes the team peter-parker’s participation in Hyperpartisan News Detection task (SemEval-2019 Task 4), which requires to classify whether a given news article is bias or not. We decided to use JAVA to do the article parsing tool and the BERT-Based model to do the bias prediction. Furthermore, we will show experiment results with analysis.
Anthology ID:
S19-2181
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:
1037–1040
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/S19-2181/
DOI:
10.18653/v1/S19-2181
Bibkey:
Cite (ACL):
Zhiyuan Ning, Yuanzhen Lin, and Ruichao Zhong. 2019. Team Peter-Parker at SemEval-2019 Task 4: BERT-Based Method in Hyperpartisan News Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1037–1040, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Team Peter-Parker at SemEval-2019 Task 4: BERT-Based Method in Hyperpartisan News Detection (Ning et al., SemEval 2019)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/S19-2181.pdf
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