{"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/joint-rnn-model-for-argument-component","title":"Joint RNN Model for Argument Component Boundary Detection","arxiv_id":"1705.02131","date":"2017-05-05","proceeding":null,"authors":["Minglan Li","Yang Gao","Hui Wen","Yang Du","Haijing Liu","Hao Wang"],"abstract":"Argument Component Boundary Detection (ACBD) is an important sub-task in\nargumentation mining; it aims at identifying the word sequences that constitute\nargument components, and is usually considered as the first sub-task in the\nargumentation mining pipeline. Existing ACBD methods heavily depend on\ntask-specific knowledge, and require considerable human efforts on\nfeature-engineering. To tackle these problems, in this work, we formulate ACBD\nas a sequence labeling problem and propose a variety of Recurrent Neural\nNetwork (RNN) based methods, which do not use domain specific or handcrafted\nfeatures beyond the relative position of the sentence in the document. In\nparticular, we propose a novel joint RNN model that can predict whether\nsentences are argumentative or not, and use the predicted results to more\nprecisely detect the argument component boundaries. We evaluate our techniques\non two corpora from two different genres; results suggest that our joint RNN\nmodel obtain the state-of-the-art performance on both datasets.","url_abs":"http://arxiv.org/abs/1705.02131v1","url_pdf":"http://arxiv.org/pdf/1705.02131v1.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":"joint-rnn-model-for-argument-component","repo_url":"https://github.com/nicoManthey/Mining-Claims-in-UNHCR-reports-A2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"boundary-detection","task_name":"Boundary Detection"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}