Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM

Chia-Lun Yeh; Babak Loni; Anne Schuth

doi:10.18653/v1/S19-2187

Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM

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Abstract

In this paper, we describe our attempt to learn bias from news articles. From our experiments, it seems that although there is a correlation between publisher bias and article bias, it is challenging to learn bias directly from the publisher labels. On the other hand, using few manually-labeled samples can increase the accuracy metric from around 60% to near 80%. Our system is computationally inexpensive and uses several standard document representations in NLP to train an SVM or LR classifier. The system ranked 4th in the SemEval-2019 task. The code is released for reproducibility.

Anthology ID:: S19-2187
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:: 1067–1071
Language:
URL:: https://aclanthology.org/S19-2187/
DOI:: 10.18653/v1/S19-2187
Bibkey:
Cite (ACL):: Chia-Lun Yeh, Babak Loni, and Anne Schuth. 2019. Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1067–1071, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):: Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM (Yeh et al., SemEval 2019)
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PDF:: https://aclanthology.org/S19-2187.pdf

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@inproceedings{yeh-etal-2019-tom,
    title = "Tom Jumbo-Grumbo at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection with {G}lo{V}e vectors and {SVM}",
    author = "Yeh, Chia-Lun  and
      Loni, Babak  and
      Schuth, Anne",
    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/S19-2187/",
    doi = "10.18653/v1/S19-2187",
    pages = "1067--1071",
    abstract = "In this paper, we describe our attempt to learn bias from news articles. From our experiments, it seems that although there is a correlation between publisher bias and article bias, it is challenging to learn bias directly from the publisher labels. On the other hand, using few manually-labeled samples can increase the accuracy metric from around 60{\%} to near 80{\%}. Our system is computationally inexpensive and uses several standard document representations in NLP to train an SVM or LR classifier. The system ranked 4th in the SemEval-2019 task. The code is released for reproducibility."
}

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%0 Conference Proceedings
%T Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM
%A Yeh, Chia-Lun
%A Loni, Babak
%A Schuth, Anne
%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 yeh-etal-2019-tom
%X In this paper, we describe our attempt to learn bias from news articles. From our experiments, it seems that although there is a correlation between publisher bias and article bias, it is challenging to learn bias directly from the publisher labels. On the other hand, using few manually-labeled samples can increase the accuracy metric from around 60% to near 80%. Our system is computationally inexpensive and uses several standard document representations in NLP to train an SVM or LR classifier. The system ranked 4th in the SemEval-2019 task. The code is released for reproducibility.
%R 10.18653/v1/S19-2187
%U https://aclanthology.org/S19-2187/
%U https://doi.org/10.18653/v1/S19-2187
%P 1067-1071

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Markdown (Informal)

[Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM](https://aclanthology.org/S19-2187/) (Yeh et al., SemEval 2019)

Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM (Yeh et al., SemEval 2019)

ACL

Chia-Lun Yeh, Babak Loni, and Anne Schuth. 2019. Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1067–1071, Minneapolis, Minnesota, USA. Association for Computational Linguistics.