{"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/um-iuling-at-semeval-2019-task-6-identifying","title":"UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs","arxiv_id":"1904.03450","date":"2019-04-06","proceeding":"SEMEVAL 2019 6","authors":["Jian Zhu","Zuoyu Tian","Sandra Kübler"],"abstract":"This paper describes the UM-IU@LING's system for the SemEval 2019 Task 6:\nOffensEval. We take a mixed approach to identify and categorize hate speech in\nsocial media. In subtask A, we fine-tuned a BERT based classifier to detect\nabusive content in tweets, achieving a macro F1 score of 0.8136 on the test\ndata, thus reaching the 3rd rank out of 103 submissions. In subtasks B and C,\nwe used a linear SVM with selected character n-gram features. For subtask C,\nour system could identify the target of abuse with a macro F1 score of 0.5243,\nranking it 27th out of 65 submissions.","url_abs":"http://arxiv.org/abs/1904.03450v1","url_pdf":"http://arxiv.org/pdf/1904.03450v1.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":"um-iuling-at-semeval-2019-task-6-identifying","repo_url":"https://github.com/zytian9/SemEval-2019-Task-6","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"abuse-detection","task_name":"Abuse Detection"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"svm","method_name":"SVM"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"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}