{"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/context-dependent-fine-grained-entity-type","title":"Context-Dependent Fine-Grained Entity Type Tagging","arxiv_id":"1412.1820","date":"2014-12-03","proceeding":null,"authors":["Dan Gillick","Nevena Lazic","Kuzman Ganchev","Jesse Kirchner","David Huynh"],"abstract":"Entity type tagging is the task of assigning category labels to each mention\nof an entity in a document. While standard systems focus on a small set of\ntypes, recent work (Ling and Weld, 2012) suggests that using a large\nfine-grained label set can lead to dramatic improvements in downstream tasks.\nIn the absence of labeled training data, existing fine-grained tagging systems\nobtain examples automatically, using resolved entities and their types\nextracted from a knowledge base. However, since the appropriate type often\ndepends on context (e.g. Washington could be tagged either as city or\ngovernment), this procedure can result in spurious labels, leading to poorer\ngeneralization. We propose the task of context-dependent fine type tagging,\nwhere the set of acceptable labels for a mention is restricted to only those\ndeducible from the local context (e.g. sentence or document). We introduce new\nresources for this task: 12,017 mentions annotated with their context-dependent\nfine types, and we provide baseline experimental results on this data.","url_abs":"http://arxiv.org/abs/1412.1820v2","url_pdf":"http://arxiv.org/pdf/1412.1820v2.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":"context-dependent-fine-grained-entity-type","repo_url":"https://github.com/INK-USC/AFET","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"context-dependent-fine-grained-entity-type","repo_url":"https://github.com/shanzhenren/AFET","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"context-dependent-fine-grained-entity-type","repo_url":"https://github.com/shanzhenren/PLE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"context-dependent-fine-grained-entity-type","repo_url":"https://github.com/sheng-z/figet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"type","task_name":"Vocal Bursts Type Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1412.1820","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}