{"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/argument-mining-with-structured-svms-and-rnns","title":"Argument Mining with Structured SVMs and RNNs","arxiv_id":"1704.06869","date":"2017-04-23","proceeding":"ACL 2017 7","authors":["Vlad Niculae","Joonsuk Park","Claire Cardie"],"abstract":"We propose a novel factor graph model for argument mining, designed for\nsettings in which the argumentative relations in a document do not necessarily\nform a tree structure. (This is the case in over 20% of the web comments\ndataset we release.) Our model jointly learns elementary unit type\nclassification and argumentative relation prediction. Moreover, our model\nsupports SVM and RNN parametrizations, can enforce structure constraints (e.g.,\ntransitivity), and can express dependencies between adjacent relations and\npropositions. Our approaches outperform unstructured baselines in both web\ncomments and argumentative essay datasets.","url_abs":"http://arxiv.org/abs/1704.06869v1","url_pdf":"http://arxiv.org/pdf/1704.06869v1.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":"argument-mining-with-structured-svms-and-rnns","repo_url":"https://github.com/vene/marseille","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"argument-mining","task_name":"Argument Mining"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"relation-prediction","task_name":"Relation Prediction"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.06869","atlas_url":"https://app.syntology.ai/?focus=1704.06869","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}