{"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/analysis-of-named-entity-recognition-and","title":"Analysis of Named Entity Recognition and Linking for Tweets","arxiv_id":"1410.7182","date":"2014-10-27","proceeding":null,"authors":["Leon Derczynski","Diana Maynard","Giuseppe Rizzo","Marieke van Erp","Genevieve Gorrell","Raphaël Troncy","Johann Petrak","Kalina Bontcheva"],"abstract":"Applying natural language processing for mining and intelligent information\naccess to tweets (a form of microblog) is a challenging, emerging research\narea. Unlike carefully authored news text and other longer content, tweets pose\na number of new challenges, due to their short, noisy, context-dependent, and\ndynamic nature. Information extraction from tweets is typically performed in a\npipeline, comprising consecutive stages of language identification,\ntokenisation, part-of-speech tagging, named entity recognition and entity\ndisambiguation (e.g. with respect to DBpedia). In this work, we describe a new\nTwitter entity disambiguation dataset, and conduct an empirical analysis of\nnamed entity recognition and disambiguation, investigating how robust a number\nof state-of-the-art systems are on such noisy texts, what the main sources of\nerror are, and which problems should be further investigated to improve the\nstate of the art.","url_abs":"http://arxiv.org/abs/1410.7182v1","url_pdf":"http://arxiv.org/pdf/1410.7182v1.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":[],"tasks":[{"task_slug":"entity-disambiguation","task_name":"Entity Disambiguation"},{"task_slug":"language-identification","task_name":"Language Identification"},{"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":"ipm-nel","name":"IPM NEL","full_name":"Derczynski IPM Named Entity Linking"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1410.7182","atlas_url":"https://app.syntology.ai/?focus=1410.7182","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}