@inproceedings{zehe-etal-2019-team,
title = "Team Xenophilius Lovegood at {S}em{E}val-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks",
author = "Zehe, Albin and
Hettinger, Lena and
Ernst, Stefan and
Hauptmann, Christian and
Hotho, Andreas",
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/S19-2183",
doi = "10.18653/v1/S19-2183",
pages = "1047--1051",
abstract = "This paper describes our system for the SemEval 2019 Task 4 on hyperpartisan news detection. We build on an existing deep learning approach for sentence classification based on a Convolutional Neural Network. Modifying the original model with additional layers to increase its expressiveness and finally building an ensemble of multiple versions of the model, we obtain an accuracy of 67.52{\%} and an F1 score of 73.78{\%} on the main test dataset. We also report on additional experiments incorporating handcrafted features into the CNN and using it as a feature extractor for a linear SVM.",
}
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%0 Conference Proceedings
%T Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks
%A Zehe, Albin
%A Hettinger, Lena
%A Ernst, Stefan
%A Hauptmann, Christian
%A Hotho, Andreas
%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 zehe-etal-2019-team
%X This paper describes our system for the SemEval 2019 Task 4 on hyperpartisan news detection. We build on an existing deep learning approach for sentence classification based on a Convolutional Neural Network. Modifying the original model with additional layers to increase its expressiveness and finally building an ensemble of multiple versions of the model, we obtain an accuracy of 67.52% and an F1 score of 73.78% on the main test dataset. We also report on additional experiments incorporating handcrafted features into the CNN and using it as a feature extractor for a linear SVM.
%R 10.18653/v1/S19-2183
%U https://aclanthology.org/S19-2183
%U https://doi.org/10.18653/v1/S19-2183
%P 1047-1051
Markdown (Informal)
[Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks](https://aclanthology.org/S19-2183) (Zehe et al., SemEval 2019)
ACL