Servicios Personalizados
Revista
Articulo
Indicadores
- Citado por SciELO
- Accesos
Links relacionados
- Similares en SciELO
Compartir
Computación y Sistemas
versión On-line ISSN 2007-9737versión impresa ISSN 1405-5546
Resumen
PORCARO, Lorenzo y SAGGION, Horacio. Recognizing Musical Entities in User-generated Content. Comp. y Sist. [online]. 2019, vol.23, n.3, pp.1079-1088. Epub 09-Ago-2021. ISSN 2007-9737. https://doi.org/10.13053/cys-23-3-3280.
Recognizing Musical Entities is important for Music Information Retrieval (MIR) since it can improve the performance of several tasks such as music recommendation, genre classification or artist similarity. However, most entity recognition systems in the music domain have concentrated on formal texts (e.g. artists' biographies, encyclopedic articles, etc.), ignoring rich and noisy user-generated content. In this work, we present a novel method to recognize musical entities in Twitter content generated by users following a classical music radio channel. Our approach takes advantage of both formal radio schedule and users' tweets to improve entity recognition. We instantiate several machine learning algorithms to perform entity recognition combining task-specific and corpus-based features. We also show how to improve recognition results by jointly considering formal and user-generated content.
Palabras llave : Named entity recognition; music information retrieval; user-generated content.