{"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/recognizing-musical-entities-in-user","title":"Recognizing Musical Entities in User-generated Content","arxiv_id":"1904.00648","date":"2019-04-01","proceeding":null,"authors":["Lorenzo Porcaro","Horacio Saggion"],"abstract":"Recognizing Musical Entities is important for Music Information Retrieval\n(MIR) since it can improve the performance of several tasks such as music\nrecommendation, genre classification or artist similarity. However, most entity\nrecognition systems in the music domain have concentrated on formal texts (e.g.\nartists' biographies, encyclopedic articles, etc.), ignoring rich and noisy\nuser-generated content. In this work, we present a novel method to recognize\nmusical entities in Twitter content generated by users following a classical\nmusic radio channel. Our approach takes advantage of both formal radio schedule\nand users' tweets to improve entity recognition. We instantiate several machine\nlearning algorithms to perform entity recognition combining task-specific and\ncorpus-based features. We also show how to improve recognition results by\njointly considering formal and user-generated content","url_abs":"http://arxiv.org/abs/1904.00648v1","url_pdf":"http://arxiv.org/pdf/1904.00648v1.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":"recognizing-musical-entities-in-user","repo_url":"https://github.com/MTG/music-ner","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"genre-classification","task_name":"Genre classification"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"music-information-retrieval","task_name":"Music Information Retrieval"},{"task_slug":"music-recommendation","task_name":"Music Recommendation"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}