{"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/neural-reranking-for-named-entity-recognition","title":"Neural Reranking for Named Entity Recognition","arxiv_id":"1707.05127","date":"2017-07-17","proceeding":"RANLP 2017 9","authors":["Jie Yang","Yue Zhang","Fei Dong"],"abstract":"We propose a neural reranking system for named entity recognition (NER). The\nbasic idea is to leverage recurrent neural network models to learn\nsentence-level patterns that involve named entity mentions. In particular,\ngiven an output sentence produced by a baseline NER model, we replace all\nentity mentions, such as \\textit{Barack Obama}, into their entity types, such\nas \\textit{PER}. The resulting sentence patterns contain direct output\ninformation, yet is less sparse without specific named entities. For example,\n\"PER was born in LOC\" can be such a pattern. LSTM and CNN structures are\nutilised for learning deep representations of such sentences for reranking.\nResults show that our system can significantly improve the NER accuracies over\ntwo different baselines, giving the best reported results on a standard\nbenchmark.","url_abs":"http://arxiv.org/abs/1707.05127v1","url_pdf":"http://arxiv.org/pdf/1707.05127v1.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":"neural-reranking-for-named-entity-recognition","repo_url":"https://github.com/jiesutd/RerankNER","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cg","task_name":"NER"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"reranking","task_name":"Reranking"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/named-entity-recognition-ner-on-conll-2003","task":"Named Entity Recognition (NER)","dataset":"CoNLL 2003 (English)","model":"Yang et al. ([2017a])","rank_in_archive_order":57,"of":73,"metrics":{"F1":"91.62"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.05127","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}