{"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/a-comparison-of-named-entity-recognition","title":"A Comparison of Named Entity Recognition Tools Applied to Biographical Texts","arxiv_id":"1308.0661","date":"2013-08-03","proceeding":null,"authors":["Samet Atdağ","Vincent Labatut"],"abstract":"Named entity recognition (NER) is a popular domain of natural language\nprocessing. For this reason, many tools exist to perform this task. Amongst\nother points, they differ in the processing method they rely upon, the entity\ntypes they can detect, the nature of the text they can handle, and their\ninput/output formats. This makes it difficult for a user to select an\nappropriate NER tool for a specific situation. In this article, we try to\nanswer this question in the context of biographic texts. For this matter, we\nfirst constitute a new corpus by annotating Wikipedia articles. We then select\npublicly available, well known and free for research NER tools for comparison:\nStanford NER, Illinois NET, OpenCalais NER WS and Alias-i LingPipe. We apply\nthem to our corpus, assess their performances and compare them. When\nconsidering overall performances, a clear hierarchy emerges: Stanford has the\nbest results, followed by LingPipe, Illionois and OpenCalais. However, a more\ndetailed evaluation performed relatively to entity types and article categories\nhighlights the fact their performances are diversely influenced by those\nfactors. This complementarity opens an interesting perspective regarding the\ncombination of these individual tools in order to improve performance.","url_abs":"http://arxiv.org/abs/1308.0661v1","url_pdf":"http://arxiv.org/pdf/1308.0661v1.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":"a-comparison-of-named-entity-recognition","repo_url":"https://github.com/CompNet/nerwip","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"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":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}