%0 Conference Proceedings %T The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4 %A Hanawa, Kazuaki %A Sasaki, Shota %A Ouchi, Hiroki %A Suzuki, Jun %A Inui, Kentaro %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 hanawa-etal-2019-sally %X This paper describes our system submitted to the formal run of SemEval-2019 Task 4: Hyperpartisan news detection. Our system is based on a linear classifier using several features, i.e., 1) embedding features based on the pre-trained BERT embeddings, 2) article length features, and 3) embedding features of informative phrases extracted from by-publisher dataset. Our system achieved 80.9% accuracy on the test set for the formal run and got the 3rd place out of 42 teams. %R 10.18653/v1/S19-2185 %U https://aclanthology.org/S19-2185 %U https://doi.org/10.18653/v1/S19-2185 %P 1057-1061