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 ...
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.
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