# Recognizing Musical Entities in User-generated Content

arXiv: https://arxiv.org/abs/1904.00648 · HTML: https://mlsanity.com/paper/1904.00648/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [A Human Subject Study of Named Entity Recognition (NER) in Conversational Music Recommendation Queries](https://mlsanity.com/paper/2303.06944/) (2023)
- [A Benchmark and Robustness Study of In-Context-Learning with Large Language Models in Music Entity Detection](https://mlsanity.com/paper/2412.11851/) (2024)
- [Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries](https://mlsanity.com/paper/2602.12301/) (2026)

## Abstract (arXiv)

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