Convolutional neural networks (CNNs) have demonstrated superior capability for extracting information from raw signals in computer vision. Recently, characterlevel and multi-channel CNNs have exhibited excellent performance for sentence classification tasks. We apply CNNs to large-scale authorship attribution, which aims to determine an unknown text's author among many candidate authors, motivated by their ability to process characterlevel signals and to differentiate between a large number of classes, while making fast predictions in comparison to state-ofthe-art approaches. We extensively evaluate CNN-based approaches that leverage word and character channels and compare them against state-of-the-art methods for a large range of author numbers, shedding new light on traditional approaches. We show that character-level CNNs outperform the state-of-the-art on four out of five datasets in different domains. Additionally, we present the first application of authorship attribution to reddit. Finally, we release our new reddit and Twitter datasets for further research.
Alternatively, REF uses convolutional neural network (CNN) to train an author classifiers.
Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution
ArXiv
ArXiv
Computer Science