{"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/authorship-attribution-using-a-neural-network","title":"Authorship Attribution Using a Neural Network Language Model","arxiv_id":"1602.05292","date":"2016-02-17","proceeding":null,"authors":["Zhenhao Ge","Yufang Sun","Mark J. T. Smith"],"abstract":"In practice, training language models for individual authors is often\nexpensive because of limited data resources. In such cases, Neural Network\nLanguage Models (NNLMs), generally outperform the traditional non-parametric\nN-gram models. Here we investigate the performance of a feed-forward NNLM on an\nauthorship attribution problem, with moderate author set size and relatively\nlimited data. We also consider how the text topics impact performance. Compared\nwith a well-constructed N-gram baseline method with Kneser-Ney smoothing, the\nproposed method achieves nearly 2:5% reduction in perplexity and increases\nauthor classification accuracy by 3:43% on average, given as few as 5 test\nsentences. The performance is very competitive with the state of the art in\nterms of accuracy and demand on test data. The source code, preprocessed\ndatasets, a detailed description of the methodology and results are available\nat https://github.com/zge/authorship-attribution.","url_abs":"http://arxiv.org/abs/1602.05292v1","url_pdf":"http://arxiv.org/pdf/1602.05292v1.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":"authorship-attribution-using-a-neural-network","repo_url":"https://github.com/zge/authorship-attribution","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"authorship-attribution","task_name":"Authorship Attribution"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1602.05292","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}