@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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<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.</abstract>
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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
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)
ACL