English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Convolutional neural networks (CNNs) have demonstrated superior capabilityfor extracting information from raw signals in computer vision. Recently,character-level and multi-channel CNNs have exhibited excellent performance forsentence classification tasks. We apply CNNs to large-scale authorshipattribution, which aims to determine an unknown text's author among manycandidate authors, motivated by their ability to process character-levelsignals and to differentiate between a large number of classes, while makingfast predictions in comparison to state-of-the-art approaches. We extensivelyevaluate CNN-based approaches that leverage word and character channels andcompare them against state-of-the-art methods for a large range of authornumbers, shedding new light on traditional approaches. We show thatcharacter-level CNNs outperform the state-of-the-art on four out of fivedatasets in different domains. Additionally, we present the first applicationof authorship attribution to reddit.

Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution