{"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/clearumor-at-semeval-2019-task-7-convolving","title":"CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors","arxiv_id":"1904.03084","date":"2019-04-05","proceeding":"SEMEVAL 2019 6","authors":["Ipek Baris","Lukas Schmelzeisen","Steffen Staab"],"abstract":"This paper describes our submission to SemEval-2019 Task 7: RumourEval:\nDetermining Rumor Veracity and Support for Rumors. We participated in both\nsubtasks. The goal of subtask A is to classify the type of interaction between\na rumorous social media post and a reply post as support, query, deny, or\ncomment. The goal of subtask B is to predict the veracity of a given rumor. For\nsubtask A, we implement a CNN-based neural architecture using ELMo embeddings\nof post text combined with auxiliary features and achieve a F1-score of 44.6%.\nFor subtask B, we employ a MLP neural network leveraging our estimates for\nsubtask A and achieve a F1-score of 30.1% (second place in the competition). We\nprovide results and analysis of our system performance and present ablation\nexperiments.","url_abs":"http://arxiv.org/abs/1904.03084v1","url_pdf":"http://arxiv.org/pdf/1904.03084v1.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":"clearumor-at-semeval-2019-task-7-convolving","repo_url":"https://github.com/lschmelzeisen/clearumor","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"rumour-detection","task_name":"Rumour Detection"}],"methods":[{"method_slug":"bilstm","method_name":"BiLSTM"},{"method_slug":"elmo","method_name":"ELMo"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"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}