{"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/fine-grained-entity-typing-through-increased","title":"Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds","arxiv_id":"1804.08000","date":"2018-04-21","proceeding":"SEMEVAL 2018 6","authors":["Sheng Zhang","Kevin Duh","Benjamin Van Durme"],"abstract":"Fine-grained entity typing is the task of assigning fine-grained semantic\ntypes to entity mentions. We propose a neural architecture which learns a\ndistributional semantic representation that leverages a greater amount of\nsemantic context -- both document and sentence level information -- than prior\nwork. We find that additional context improves performance, with further\nimprovements gained by utilizing adaptive classification thresholds.\nExperiments show that our approach without reliance on hand-crafted features\nachieves the state-of-the-art results on three benchmark datasets.","url_abs":"http://arxiv.org/abs/1804.08000v1","url_pdf":"http://arxiv.org/pdf/1804.08000v1.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":"fine-grained-entity-typing-through-increased","repo_url":"https://github.com/sheng-z/figet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"entity-typing","task_name":"Entity Typing"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.08000","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}