# Natural Language Processing for Music Knowledge Discovery

arXiv: https://arxiv.org/abs/1807.02200 · HTML: https://mlsanity.com/paper/1807.02200/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

## Built on by (most cited first, 6 of 6)

- [Explainability in Music Recommender Systems](https://mlsanity.com/paper/2201.10528/) (2022)
- [Evaluation of pretrained language models on music understanding](https://mlsanity.com/paper/2409.11449/) (2024)
- [Docent: A content-based recommendation system to discover contemporary art](https://mlsanity.com/paper/2207.05648/) (2022)
- [Joint Learning of Wording and Formatting for Singable Melody-to-Lyric Generation](https://mlsanity.com/paper/2307.02146/) (2023)
- [Towards mapping the contemporary art world with ArtLM: an art-specific NLP model](https://mlsanity.com/paper/2212.07127/) (2022)
- [Linguistic Complexity and Socio-cultural Patterns in Hip-Hop Lyrics](https://mlsanity.com/paper/2505.00035/) (2025)

## Abstract (arXiv)

Today, a massive amount of musical knowledge is stored in written form, with testimonies dated as far back as several centuries ago. In this work, we present different Natural Language Processing (NLP) approaches to harness the potential of these text collections for automatic music knowledge discovery, covering different phases in a prototypical NLP pipeline, namely corpus compilation, text-mining, information extraction, knowledge graph generation and sentiment analysis. Each of these approaches is presented alongside different use cases (i.e., flamenco, Renaissance and popular music) where large collections of documents are processed, and conclusions stemming from data-driven analyses are presented and discussed.