| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560434 |
| ISSN: | 1405-5546 |
| Autores: | Porcaro, Lorenzo1 Saggion, Horacio2 |
| Instituciones: | 1Universitat Pompeu Fabra, Music Technology Group, Barcelona. España 2Universitat Pompeu Fabra, TALN Natural Language Processing Group, Barcelona. España |
| Año: | 2019 |
| Periodo: | Jul-Sep |
| Volumen: | 23 |
| Número: | 3 |
| Paginación: | 1079-1088 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | 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. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Named entity recognition, Music information retrieval, User-generated content, Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
