@inproceedings{papadopoulou-etal-2019-brenda,
title = "Brenda Starr at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
author = "Papadopoulou, Olga and
Kordopatis-Zilos, Giorgos and
Zampoglou, Markos and
Papadopoulos, Symeon and
Kompatsiaris, Yiannis",
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/S19-2157",
doi = "10.18653/v1/S19-2157",
pages = "924--928",
abstract = "In the effort to tackle the challenge of Hyperpartisan News Detection, i.e., the task of deciding whether a news article is biased towards one party, faction, cause, or person, we experimented with two systems: i) a standard supervised learning approach using superficial text and bag-of-words features from the article title and body, and ii) a deep learning system comprising a four-layer convolutional neural network and max-pooling layers after the embedding layer, feeding the consolidated features to a bi-directional recurrent neural network. We achieved an F-score of 0.712 with our best approach, which corresponds to the mid-range of performance levels in the leaderboard.",
}
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%0 Conference Proceedings
%T Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection
%A Papadopoulou, Olga
%A Kordopatis-Zilos, Giorgos
%A Zampoglou, Markos
%A Papadopoulos, Symeon
%A Kompatsiaris, Yiannis
%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 papadopoulou-etal-2019-brenda
%X In the effort to tackle the challenge of Hyperpartisan News Detection, i.e., the task of deciding whether a news article is biased towards one party, faction, cause, or person, we experimented with two systems: i) a standard supervised learning approach using superficial text and bag-of-words features from the article title and body, and ii) a deep learning system comprising a four-layer convolutional neural network and max-pooling layers after the embedding layer, feeding the consolidated features to a bi-directional recurrent neural network. We achieved an F-score of 0.712 with our best approach, which corresponds to the mid-range of performance levels in the leaderboard.
%R 10.18653/v1/S19-2157
%U https://aclanthology.org/S19-2157
%U https://doi.org/10.18653/v1/S19-2157
%P 924-928
Markdown (Informal)
[Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection](https://aclanthology.org/S19-2157) (Papadopoulou et al., SemEval 2019)
ACL
- Olga Papadopoulou, Giorgos Kordopatis-Zilos, Markos Zampoglou, Symeon Papadopoulos, and Yiannis Kompatsiaris. 2019. Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 924–928, Minneapolis, Minnesota, USA. Association for Computational Linguistics.