@inproceedings{moreno-etal-2019-rouletabille,
title = "Rouletabille at {S}em{E}val-2019 Task 4: Neural Network Baseline for Identification of Hyperpartisan Publishers",
author = "Moreno, Jose G. and
Pitarch, Yoann and
Pinel-Sauvagnat, Karen and
Hubert, Gilles",
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/S19-2169",
doi = "10.18653/v1/S19-2169",
pages = "981--984",
abstract = "This paper describes the Rouletabille participation to the Hyperpartisan News Detection task. We propose the use of different text classification methods for this task. Preliminary experiments using a similar collection used in (Potthast et al., 2018) show that neural-based classification methods reach state-of-the art results. Our final submission is composed of a unique run that ranks among all runs at 3/49 position for the by-publisher test dataset and 43/96 for the by-article test dataset in terms of Accuracy.",
}
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<abstract>This paper describes the Rouletabille participation to the Hyperpartisan News Detection task. We propose the use of different text classification methods for this task. Preliminary experiments using a similar collection used in (Potthast et al., 2018) show that neural-based classification methods reach state-of-the art results. Our final submission is composed of a unique run that ranks among all runs at 3/49 position for the by-publisher test dataset and 43/96 for the by-article test dataset in terms of Accuracy.</abstract>
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%0 Conference Proceedings
%T Rouletabille at SemEval-2019 Task 4: Neural Network Baseline for Identification of Hyperpartisan Publishers
%A Moreno, Jose G.
%A Pitarch, Yoann
%A Pinel-Sauvagnat, Karen
%A Hubert, Gilles
%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 moreno-etal-2019-rouletabille
%X This paper describes the Rouletabille participation to the Hyperpartisan News Detection task. We propose the use of different text classification methods for this task. Preliminary experiments using a similar collection used in (Potthast et al., 2018) show that neural-based classification methods reach state-of-the art results. Our final submission is composed of a unique run that ranks among all runs at 3/49 position for the by-publisher test dataset and 43/96 for the by-article test dataset in terms of Accuracy.
%R 10.18653/v1/S19-2169
%U https://aclanthology.org/S19-2169
%U https://doi.org/10.18653/v1/S19-2169
%P 981-984
Markdown (Informal)
[Rouletabille at SemEval-2019 Task 4: Neural Network Baseline for Identification of Hyperpartisan Publishers](https://aclanthology.org/S19-2169) (Moreno et al., SemEval 2019)
ACL