{"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/high-accuracy-rule-based-question","title":"High Accuracy Rule-based Question Classification using Question Syntax and Semantics","arxiv_id":null,"date":"2016-12-01","proceeding":"COLING 2016 12","authors":["Harish Tayyar Madabushi","Mark Lee"],"abstract":"We present in this paper a purely rule-based system for Question Classification which we divide into two parts: The first is the extraction of relevant words from a question by use of its structure, and the second is the classification of questions based on rules that associate these words to Concepts. We achieve an accuracy of 97.2{\\%}, close to a 6 point improvement over the previous State of the Art of 91.6{\\%}. Additionally, we believe that machine learning algorithms can be applied on top of this method to further improve accuracy.","url_abs":"https://aclanthology.org/C16-1116","url_pdf":"https://aclanthology.org/C16-1116.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":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-classification-on-trec-50","task":"Text Classification","dataset":"TREC-50","model":"Rules","rank_in_archive_order":1,"of":2,"metrics":{"Error":"2.8"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}