  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
