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.
Cite
CITATION STYLE
Zehe, A., Hettinger, L., Ernst, S., Hauptmann, C., & Hotho, A. (2019). Team Xenophilius Lovegood at SemEval-2019 task 4: Hyperpartisanship classification using convolutional neural networks. In NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop (pp. 1047–1051). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s19-2183
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.