# Recognizing Musical Entities in User-generated Content

## TL;DR

This paper introduces a new method for recognizing musical entities in noisy user-generated Twitter content, leveraging both formal radio schedules and user tweets to enhance accuracy in music information retrieval tasks.

## Contribution

It presents a novel approach combining formal and informal content for musical entity recognition, utilizing multiple machine learning algorithms with task-specific and corpus-based features.

## Key findings

- Improved recognition accuracy by integrating formal and user-generated content
- Effective machine learning models for noisy social media text
- Enhanced performance in music information retrieval tasks

## Abstract

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