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Back Porcaro L, Saggion H. Recognizing Musical Entities in User-generated Content. International Conference on Computational Linguistics and Intelligent Text Processing (CICLing) 2019

Porcaro L, Saggion H. Recognizing Musical Entities in User-generated Content. International Conference on Computational Linguistics and Intelligent Text Processing (CICLing) 2019

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.

Additional material

- Dataset: 5,093 automatically generated tweets by the BBC Radio 3 Music Bot available at https://github.com/LPorcaro/musicner

- Code: https://github.com/LPorcaro/musicner

Link: https://arxiv.org/abs/1904.00648