{"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/a-supervised-approach-to-the-interpretation","title":"A Supervised Approach To The Interpretation Of Imperative To-Do Lists","arxiv_id":"1806.07999","date":"2018-06-20","proceeding":null,"authors":["Paul Landes","Barbara Di Eugenio"],"abstract":"To-do lists are a popular medium for personal information management. As\nto-do tasks are increasingly tracked in electronic form with mobile and desktop\norganizers, so does the potential for software support for the corresponding\ntasks by means of intelligent agents. While there has been work in the area of\npersonal assistants for to-do tasks, no work has focused on classifying user\nintention and information extraction as we do. We show that our methods perform\nwell across two corpora that span sub-domains, one of which we released.","url_abs":"http://arxiv.org/abs/1806.07999v1","url_pdf":"http://arxiv.org/pdf/1806.07999v1.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":"a-supervised-approach-to-the-interpretation","repo_url":"https://github.com/plandes/todo-task","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}