The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4

2019-06-01 · SEMEVAL 2019 6 · Kazuaki Hanawa, Shota Sasaki, Hiroki Ouchi, Jun Suzuki, Kentaro Inui

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.

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This paper describes the Pioquinto Manterola Hyperpartisan News Detector, which participated in SemEval-2019 Task 4. Hyperpartisan news is highly polarized and takes a very biased or one{--}sided view of a particular sto…

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Team Jack Ryder at SemEval-2019 Task 4: Using BERT Representations for Detecting Hyperpartisan News

2019-06-01 · SEMEVAL 2019 6 · Daniel Shaprin, Giovanni Da San Martino, Alberto Barr{\'o}n-Cede{\~n}o, Preslav Nakov

We describe the system submitted by the Jack Ryder team to SemEval-2019 Task 4 on Hyperpartisan News Detection. The task asked participants to predict whether a given article is hyperpartisan, i.e., extreme-left or extre…

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