Tutorial - Natural Language Processing for Music Information Retrieval Tutorial - Natural Language Processing for Music Information Retrieval

Tutorial - Natural Language Processing for Music Information Retrieval

  • Date: January 30th 2017. 14:30h - 17:30h
  • Location: Poblenou Campus, UPF (Roc Boronat 138, Barcelona). Room 52.S27
  • Tutorial presenters: Sergio Oramas, Luis Espinosa (Music meets NLP project)


Updated material may be found here

This tutorial is an updated version of the tutorial presented at ISMIR2016.


Sergio Oramas, Luies Espinosa-Anke, Shuo Zhang, Horacio Saggion & Xavier Serra (2016). Natural Language Processing for Music Information Retrieval. 17th International Society for Music Information Retrieval Conference (ISMIR 2016).


An increasing amount of musical information is being published daily in media like Social Networks, Digital Libraries or Web Pages. All this data has the potential to impact in musicological studies, as well as tasks within MIR such as music recommendation. Making sense of it is a very challenging task, and so this tutorial aims to provide the audience with potential applications of Natural Language Processing (NLP) to MIR and Computational Musicology.

In this tutorial, we focus on linguistic, semantic and statistical-­based approaches to extract and formalize knowledge about music from naturally occurring text. We propose to provide the audience with a preliminary introduction to NLP, covering its main tasks along with the state­-of-­the-­art and most recent developments. In addition, we will showcase the main challenges that the music domain poses to the different NLP tasks, and the already developed methodologies for leveraging them in MIR and musicological applications.

Topics covered in the tutorial:

  • Basic text preprocessing and normalization
  • Linguistic enrichment in the form of part-­of-­speech tagging, as well as shallow and 
dependency parsing.
  • Information Extraction, with special focus on Entity Linking and Relation Extraction.
  • Sentiment Analysis
  • Lexical Semantics (word embeddings)
  • Deep Learning
  • Applications in MIR
  • Applications in Musicology


  • General Introduction

  • Introduction to NLP

  • Information extraction

  • Construction of Music Knowledge Bases (KBs)
  • Semantic Enrichment of musical texts
  • Applications in Music Information Retrieval (MIR)
  • Applications in Musicology

  • Lexical Semantics

  • Deep Learning

  • Conclusions and future work