{"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/zo-grof-a-comprehensive-corpus-for-offensive","title":"“Zo Grof !”: A Comprehensive Corpus for Offensive and Abusive Language in Dutch","arxiv_id":null,"date":"2022-07-01","proceeding":"NAACL (WOAH) 2022 7","authors":["Ward Ruitenbeek","Victor Zwart","Robin Van Der Noord","Zhenja Gnezdilov","Tommaso Caselli"],"abstract":"This paper presents a comprehensive corpus for the study of socially unacceptable language in Dutch. The corpus extends and revise an existing resource with more data and introduces a new annotation dimension for offensive language, making it a unique resource in the Dutch language panorama. Each language phenomenon (abusive and offensive language) in the corpus has been annotated with a multi-layer annotation scheme modelling the explicitness and the target(s) of the message. We have conducted a new set of experiments with different classification algorithms on all annotation dimensions. Monolingual Pre-Trained Language Models prove as the best systems, obtaining a macro-average F1 of 0.828 for binary classification of offensive language, and 0.579 for the targets of offensive messages. Furthermore, the best system obtains a macro-average F1 of 0.667 for distinguishing between abusive and offensive messages.","url_abs":"https://aclanthology.org/2022.woah-1.5","url_pdf":"https://aclanthology.org/2022.woah-1.5.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":"zo-grof-a-comprehensive-corpus-for-offensive","repo_url":"https://github.com/tommasoc80/dalc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"abusive-language","task_name":"Abusive Language"},{"task_slug":"binary-classification","task_name":"Binary Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}