{"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/soft-gazetteers-for-low-resource-named-entity","title":"Soft Gazetteers for Low-Resource Named Entity Recognition","arxiv_id":"2005.01866","date":"2020-05-04","proceeding":"ACL 2020 6","authors":["Shruti Rijhwani","Shuyan Zhou","Graham Neubig","Jaime Carbonell"],"abstract":"Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-crafted features for strong performance, recent work has demonstrated their utility for named entity recognition on English data. However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages. To address this problem, we propose a method of \"soft gazetteers\" that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score. Code and data are available at https://github.com/neulab/soft-gazetteers.","url_abs":"https://arxiv.org/abs/2005.01866v1","url_pdf":"https://arxiv.org/pdf/2005.01866v1.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":"soft-gazetteers-for-low-resource-named-entity","repo_url":"https://github.com/neulab/soft-gazetteers","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cross-lingual-entity-linking","task_name":"Cross-Lingual Entity Linking"},{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"low-resource-named-entity-recognition","task_name":"Low Resource Named Entity Recognition"},{"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":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2005.01866","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}