{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/character-level-and-multi-channel","title":"Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution","arxiv_id":"1609.06686","date":"2016-09-21","proceeding":null,"authors":["Sebastian Ruder","Parsa Ghaffari","John G. Breslin"],"abstract":"Convolutional neural networks (CNNs) have demonstrated superior capability\nfor extracting information from raw signals in computer vision. Recently,\ncharacter-level and multi-channel CNNs have exhibited excellent performance for\nsentence classification tasks. We apply CNNs to large-scale authorship\nattribution, which aims to determine an unknown text's author among many\ncandidate authors, motivated by their ability to process character-level\nsignals and to differentiate between a large number of classes, while making\nfast predictions in comparison to state-of-the-art approaches. We extensively\nevaluate CNN-based approaches that leverage word and character channels and\ncompare them against state-of-the-art methods for a large range of author\nnumbers, shedding new light on traditional approaches. We show that\ncharacter-level CNNs outperform the state-of-the-art on four out of five\ndatasets in different domains. Additionally, we present the first application\nof authorship attribution to reddit.","url_abs":"http://arxiv.org/abs/1609.06686v1","url_pdf":"http://arxiv.org/pdf/1609.06686v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"character-level-and-multi-channel","repo_url":"https://github.com/anutkk/RambaNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"character-level-and-multi-channel","repo_url":"https://github.com/asad1996172/Authorship-attribution-using-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"character-level-and-multi-channel","repo_url":"https://gitlab.com/tony-hn/stylomet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"authorship-attribution","task_name":"Authorship Attribution"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-classification","task_name":"Sentence Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.06686","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}