{"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/user-intent-prediction-in-information-seeking","title":"User Intent Prediction in Information-seeking Conversations","arxiv_id":"1901.03489","date":"2019-01-11","proceeding":null,"authors":["Chen Qu","Liu Yang","Bruce Croft","Yongfeng Zhang","Johanne R. Trippas","Minghui Qiu"],"abstract":"Conversational assistants are being progressively adopted by the general\npopulation. However, they are not capable of handling complicated\ninformation-seeking tasks that involve multiple turns of information exchange.\nDue to the limited communication bandwidth in conversational search, it is\nimportant for conversational assistants to accurately detect and predict user\nintent in information-seeking conversations. In this paper, we investigate two\naspects of user intent prediction in an information-seeking setting. First, we\nextract features based on the content, structural, and sentiment\ncharacteristics of a given utterance, and use classic machine learning methods\nto perform user intent prediction. We then conduct an in-depth feature\nimportance analysis to identify key features in this prediction task. We find\nthat structural features contribute most to the prediction performance. Given\nthis finding, we construct neural classifiers to incorporate context\ninformation and achieve better performance without feature engineering. Our\nfindings can provide insights into the important factors and effective methods\nof user intent prediction in information-seeking conversations.","url_abs":"http://arxiv.org/abs/1901.03489v1","url_pdf":"http://arxiv.org/pdf/1901.03489v1.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":"user-intent-prediction-in-information-seeking","repo_url":"https://github.com/prdwb/UserIntentPrediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"conversational-search","task_name":"Conversational Search"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"feature-importance","task_name":"Feature Importance"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.03489","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}