{"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/named-entity-recognition-in-tweets-an","title":"Named Entity Recognition in Tweets: An Experimental Study","arxiv_id":null,"date":"2011-07-01","proceeding":"Conference on Empirical Methods in Natural Language Processing 2011 7","authors":["Alan Ritter","Sam Clark","Mausam Etzioni","Oren Etzioni"],"abstract":"People tweet more than 100 Million times\r\ndaily, yielding a noisy, informal, but sometimes informative corpus of 140-character\r\nmessages that mirrors the zeitgeist in an unprecedented manner. The performance of\r\nstandard NLP tools is severely degraded on\r\ntweets. This paper addresses this issue by\r\nre-building the NLP pipeline beginning with\r\npart-of-speech tagging, through chunking, to\r\nnamed-entity recognition. Our novel T-NER\r\nsystem doubles F1 score compared with the\r\nStanford NER system. T-NER leverages the\r\nredundancy inherent in tweets to achieve this\r\nperformance, using LabeledLDA to exploit\r\nFreebase dictionaries as a source of distant\r\nsupervision. LabeledLDA outperforms cotraining, increasing F1 by 25% over ten common entity types.\r\nOur NLP tools are available at: http://\r\ngithub.com/aritter/twitter_nlp","url_abs":"https://aclanthology.org/D11-1141.pdf","url_pdf":"https://aclanthology.org/D11-1141.pdf.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":"named-entity-recognition-in-tweets-an","repo_url":"https://github.com/aritter/twitter_nlp","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"chunking","task_name":"Chunking"},{"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":"part-of-speech-tagging","task_name":"Part-Of-Speech Tagging"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[{"slug":"ritter-pos","name":"Ritter PoS","full_name":"Ritter Twitter part-of-speech tagging"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}