{"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/efficient-dependency-guided-named-entity","title":"Efficient Dependency-Guided Named Entity Recognition","arxiv_id":"1810.08436","date":"2018-10-19","proceeding":null,"authors":["Zhanming Jie","Aldrian Obaja Muis","Wei Lu"],"abstract":"Named entity recognition (NER), which focuses on the extraction of\nsemantically meaningful named entities and their semantic classes from text,\nserves as an indispensable component for several down-stream natural language\nprocessing (NLP) tasks such as relation extraction and event extraction.\nDependency trees, on the other hand, also convey crucial semantic-level\ninformation. It has been shown previously that such information can be used to\nimprove the performance of NER (Sasano and Kurohashi 2008, Ling and Weld 2012).\nIn this work, we investigate on how to better utilize the structured\ninformation conveyed by dependency trees to improve the performance of NER.\nSpecifically, unlike existing approaches which only exploit dependency\ninformation for designing local features, we show that certain global\nstructured information of the dependency trees can be exploited when building\nNER models where such information can provide guided learning and inference.\nThrough extensive experiments, we show that our proposed novel\ndependency-guided NER model performs competitively with models based on\nconventional semi-Markov conditional random fields, while requiring\nsignificantly less running time.","url_abs":"http://arxiv.org/abs/1810.08436v2","url_pdf":"http://arxiv.org/pdf/1810.08436v2.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":"efficient-dependency-guided-named-entity","repo_url":"https://github.com/allanj/dependency-guided-ner","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"event-extraction","task_name":"Event Extraction"},{"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":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.08436","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}