{"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/combination-of-multiple-deep-learning","title":"Combination of multiple Deep Learning architectures for Offensive Language Detection in Tweets","arxiv_id":"1903.08734","date":"2019-03-16","proceeding":null,"authors":["Nicolò Frisiani","Alexis Laignelet","Batuhan Güler"],"abstract":"This report contains the details regarding our submission to the OffensEval\n2019 (SemEval 2019 - Task 6). The competition was based on the Offensive\nLanguage Identification Dataset. We first discuss the details of the classifier\nimplemented and the type of input data used and pre-processing performed. We\nthen move onto critically evaluating our performance. We have achieved a\nmacro-average F1-score of 0.76, 0.68, 0.54, respectively for Task a, Task b,\nand Task c, which we believe reflects on the level of sophistication of the\nmodels implemented. Finally, we will be discussing the difficulties encountered\nand possible improvements for the future.","url_abs":"http://arxiv.org/abs/1903.08734v2","url_pdf":"http://arxiv.org/pdf/1903.08734v2.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":"combination-of-multiple-deep-learning","repo_url":"https://github.com/alaignelet/nlp-sem-eval-2019","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"language-identification","task_name":"Language Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}