{"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/an-effective-discourse-parser-that-uses-rich","title":"An effective Discourse Parser that uses Rich Linguistic Information","arxiv_id":null,"date":"2009-05-31","proceeding":"Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 566–574, Boulder, Colorado. Association for Computational Linguistics. 2009 5","authors":["Rajen Subba","Barbara Di Eugenio"],"abstract":"This paper presents a first-order logic learning approach to determine rhetorical relations between discourse segments. Beyond linguistic cues and lexical information, our approach exploits compositional semantics and segment discourse structure data. We report a statistically significant improvement in classifying relations over attribute-value learning paradigms such as Decision Trees, RIPPER and Naive Bayes. For discourse parsing, our modified shift-reduce parsing model that uses our relation classifier significantly\r\noutperforms a right-branching majority-class baseline.","url_abs":"https://aclanthology.org/N09-1064/","url_pdf":"https://aclanthology.org/N09-1064.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":[],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"discourse-parsing","task_name":"Discourse Parsing"}],"methods":[],"datasets_introduced":[{"slug":"instructional-dt-instr-dt","name":"Instructional-DT (Instr-DT)","full_name":"Instructional Discourse Treebank"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}