{"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-recognition-with-reduced","title":"Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage","arxiv_id":"1904.13178","date":"2019-04-30","proceeding":"AKBC 2019","authors":["Abhishek Abhishek","Sanya Bathla Taneja","Garima Malik","Ashish Anand","Amit Awekar"],"abstract":"Fine-grained Entity Recognition (FgER) is the task of detecting and\nclassifying entity mentions to a large set of types spanning diverse domains\nsuch as biomedical, finance and sports. We observe that when the type set spans\nseveral domains, detection of entity mention becomes a limitation for\nsupervised learning models. The primary reason being lack of dataset where\nentity boundaries are properly annotated while covering a large spectrum of\nentity types. Our work directly addresses this issue. We propose Heuristics\nAllied with Distant Supervision (HAnDS) framework to automatically construct a\nquality dataset suitable for the FgER task. HAnDS framework exploits the high\ninterlink among Wikipedia and Freebase in a pipelined manner, reducing\nannotation errors introduced by naively using distant supervision approach.\nUsing HAnDS framework, we create two datasets, one suitable for building FgER\nsystems recognizing up to 118 entity types based on the FIGER type hierarchy\nand another for up to 1115 entity types based on the TypeNet hierarchy. Our\nextensive empirical experimentation warrants the quality of the generated\ndatasets. Along with this, we also provide a manually annotated dataset for\nbenchmarking FgER systems.","url_abs":"http://arxiv.org/abs/1904.13178v1","url_pdf":"http://arxiv.org/pdf/1904.13178v1.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-recognition-with-reduced","repo_url":"https://github.com/abhipec/HAnDS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}