Below the list of projects cofunded by the María de Maeztu program (selected via internal calls, in this link the first one launched at the beginning of the program, and in this link the second one, launched in September 2016).
In addition, the program supported:
- joint calls for cooperation between DTIC and the UPF Department of Experimental and Health Sciences (CEXS), also recognised as a María de Maeztu Unit of Excellence. Here the link to the second call (November 2017). The first call took place in January 2017.
- its own Open Science and Innovation program
- a pilot program to promote educational research collaborations with industry
The detail of the internal procedures for the distribution of funds associated to the program can be found here
Music meets Natural Language Processing
Music meets Natural Language Processing
Music meets Natural Language Processing
Today, we are witnessing an unprecedented information explosion thanks to the dramatic technological advancement brought by the Information Age. This technological (r)evolution has set the foundations for the release and publication of huge amounts of data onto online repositories such as web pages, forums, wikis and social media. Art and culture have benefited dramatically from this context, which allows potentially anyone with an available Internet connection to access, produce, publish, comment or interact with any form of media.
In this context, the music domain has attracted interest from diverse fields such as Computer Science, Music, Musicology, AI, Maths or Linguistics. In our project Music Meets Natural Language Processing we aim at representing what humans say about music, discovering and extracting knowledge from available textual sources, instead of audio and symbolic sources, as it has been studied earlier. This knowledge can be exploited to create large-scale, high quality software libraries and datasets in the music domain. For this, we will combine complementary expertise from the PI and team members in both Music, Musicology and Music Information Retrieval (MIR), along with Natural Language Processing (NLP) and Web Search & Data Mining (WSDM).
Specifically, we aim at bringing together NLP, WSDM and MIR in order to create richer repositories and software, as well as ad-hoc methods relevant to the music domain. We acknowledge the fact that Music is an artistic and scientific area which requires its own approaches and tools to cope with those cases in which generic NLP methods may fall short. One of the most exciting avenues in MIR is the exploitation of freely occurring and noisy data, thus parting ways from approaches which solely rely on structured information present in Knowledge Bases like DBpedia or Freebase. We need to account for the idiosyncratic challenges it poses (e.g. songs, albums and bands having the same name) as well as its potential applications (e.g. music recommendation or semantic search). Our focus is on knowledge extraction and exploitation from unstructured text sources. More specifically our work is focused in the following areas, Named Entity Linking (EL), Relation Extraction, Opinion Mining, entity-based Retrieval and Recommendation, and Music Information Retrieval (MIR).
To know more:
- Presentation of the project at the Data-driven Knowledge Extraction Workshop, June 2016 (Slides)
Principal researchers
Ricardo Baeza-YatesResearchers
Sergio Oramas Luis Espinosa-Anke Xavier Serra Horacio SaggionRelated Assets:
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Espinosa-Anke, L., Oramas S., Saggion H., & Serra X. ELMDist: A vector space model with words and MusicBrainz entities. Workshop on Semantic Deep Learning (SemDeep), collocated with ESWC 2017
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[TEXT] MuMu: Multimodal Music Dataset
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Oramas S., Nieto O., Sordo M., & Serra X. (2017) A Deep Multimodal Approach for Cold-start Music Recommendation. 2nd Workshop on Deep Learning for Recommender Systems, RecSys 2017
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[TEXT] MSD-A
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TARTARUS: Deep Learning for audio and text
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Best presentation award at ISMIR 2017
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[AUDIO FEATURES AND IMAGES] MSD-I: Million Song Dataset with Images for Multimodal Genre Classification
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Oramas S, Ferraro A, Correya A, Serra X. MEL: A music entity linking system. 18th International Society for Music Information Retrieval Conference (ISMIR17)
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Speck R, Röder M, Oramas S, Espinosa-Anke L, Ngonga Ngomo AC. Open Knowledge Extraction Challenge 2017. Semantic Web Challenges. SemWebEval 2017. Communications in Computer and Information Science
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[PhD thesis] Knowledge Extraction and Representation Learning for Music Recommendation and Classification
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Oramas, S, Nieto O, Barbieri F, Serra X. Multi-label Music Genre Classification from Audio, Text and Images Using Deep Features. 18th International Society for Music Information Retrieval Conference (ISMIR 2017)
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[TEXT] ELMD: Entity Linking for the Music Domain
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[TEXT] MARD: Multimodal Album Reviews Dataset
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Oramas S., Espinosa-Anke L., Sordo M., Saggion H., Serra X. Information extraction for knowledge base construction in the music domain. Data and Knowledge Engineering.
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[TEXT] KGRec-music - Music Recommendation Dataset
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[PhD thesis] Knowledge acquisition in the information age: the interplay between lexicography and natural language processing
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Challenge: Focused Musical NE Recognition and Linking in next European Semantic Web Conference - Open Knowledge Extraction Challenge
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Oramas, S., Espinosa-Anke L., Lawlor A., Serra X., & Saggion H. Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies. 17th International Society for Music Information Retrieval Conference (ISMIR'16)
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Espinosa-Anke L, Carlini R, Ronzano F, Saggion H. DEFEXT: A Semi Supervised Definition Extraction Tool. Globalex: Lexicographic Resources for Human Language Technology
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Oramas S, Espinosa-Anke L, Sordo M, Saggion H, Serra X. ELMD: An Automatically Generated Entity Linking Gold Standard Dataset in the Music Domain. Proceedings of the Language Resource and Evaluation Conference 2016
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Oramas, S, Ostuni V C, Di Noia T, Serra X, Di Sciascio E. Sound and Music Recommendation with Knowledge Graphs. ACM Transactions on Intelligent Systems and Technology (TIST)
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Oramas S, Espinosa-Anke L, Gómez F, Serra X. Natural language processing for music knowledge discovery. Journal New Music Research
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25th anniversary of the Music Technology Group
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Camacho-Collados J, Delli Bovi C, Espinosa-Anke L, Oramas S, Pasini T, Santus E, Shwartz V, Navigli R, Saggion H. SemEval-2018 Task 9: Hypernym Discovery. Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval 2018)
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Oramas S, Barbieri F, Nieto O, Serra X. Multimodal Deep Learning for Music Genre Classification. Transactions of the International Society for Music Information Retrieval
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Tutorial - Natural Language Processing for Music Information Retrieval at ISMIR2016
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[TEXT] KGRec-sound - Sound recommendation dataset
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Workshop on music knowledge extraction using machine learning on December 4th