{"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/a-data-oriented-model-of-literary-language","title":"A Data-Oriented Model of Literary Language","arxiv_id":"1701.03329","date":"2017-01-12","proceeding":"EACL 2017 4","authors":["Andreas van Cranenburgh","Rens Bod"],"abstract":"We consider the task of predicting how literary a text is, with a gold\nstandard from human ratings. Aside from a standard bigram baseline, we apply\nrich syntactic tree fragments, mined from the training set, and a series of\nhand-picked features. Our model is the first to distinguish degrees of highly\nand less literary novels using a variety of lexical and syntactic features, and\nexplains 76.0 % of the variation in literary ratings.","url_abs":"http://arxiv.org/abs/1701.03329v2","url_pdf":"http://arxiv.org/pdf/1701.03329v2.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":"a-data-oriented-model-of-literary-language","repo_url":"https://github.com/andreasvc/literariness","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}