In a world of information operations, influence campaigns, and fake news, classification of news articles as following hyperpartisan argumentation or not is becoming increasingly important. We present a deep learning-based approach in which a pre-trained language model has been fine-tuned on domain-specific data and used for classification of news articles, as part of the SemEval-2019 task on hyperpartisan news detection. The suggested approach yields accuracy and F1-scores around 0.8 which places the best performing classifier among the top-5 systems in the competition.
Neural context embeddings for automatic discovery of word senses
Mikael Kågebäck | Fredrik Johansson | Richard Johansson | Devdatt Dubhashi
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing