Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics

Song lyrics pose unique challenges for semantic similarity assessment due to their metaphorical language, structural patterns, and cultural nuances - characteristics that often challenge standard natural language processing (NLP) approaches. These challenges stem from a tension between compositional...

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Detalles Bibliográficos
Autores: Ghajari Espinosa, Adrián, Benito Santos, Alejandro, Ros Muñoz, Salvador, Fresno Fernández, Víctor Diego, González-Blanco García, Elena
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/26536
Acceso en línea:https://hdl.handle.net/20.500.14468/26536
Access Level:acceso abierto
Palabra clave:33 Ciencias Tecnológicas
compositional distributional semantics
semantic textual similarity
word embeddings
song lyrics
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spelling Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyricsGhajari Espinosa, AdriánBenito Santos, AlejandroRos Muñoz, SalvadorFresno Fernández, Víctor DiegoGonzález-Blanco García, Elena33 Ciencias Tecnológicascompositional distributional semanticssemantic textual similarityword embeddingssong lyricsSong lyrics pose unique challenges for semantic similarity assessment due to their metaphorical language, structural patterns, and cultural nuances - characteristics that often challenge standard natural language processing (NLP) approaches. These challenges stem from a tension between compositional and distributional semantics: while lyrics follow compositional structures, their meaning depends heavily on context and interpretation. The Information Theory-based Compositional Distributional Semantics framework offers a principled approach by integrating information theory with compositional rules and distributional representations. We evaluate eight embedding models on Spanish song lyrics, including multilingual, monolingual contextual, and static embeddings. Results show that multilingual models consistently outperform monolingual alternatives, with the domain-adapted ALBERTI achieving the highest F1 macro scores (78.92 ± 10.86). Our analysis reveals that monolingual models generate highly anisotropic embedding spaces, significantly impacting performance with traditional metrics. The Information Contrast Model metric proves particularly effective, providing improvements up to 18.04 percentage points over cosine similarity. Additionally, composition functions maintaining longer accumulated vector norms consistently outperform standard averaging approaches. Our findings have important implications for NLP applications and challenge standard practices in similarity calculation, showing that effectiveness varies with both task nature and model characteristics.ELSEVIERe-Spacio UNED20252025-05-1320252025-06-1520252025-06-15journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/26536reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/deed.esoai:e-spacio.uned.es:20.500.14468/265362026-06-06T12:38:31Z
dc.title.none.fl_str_mv Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics
title Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics
spellingShingle Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics
Ghajari Espinosa, Adrián
33 Ciencias Tecnológicas
compositional distributional semantics
semantic textual similarity
word embeddings
song lyrics
title_short Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics
title_full Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics
title_fullStr Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics
title_full_unstemmed Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics
title_sort Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics
dc.creator.none.fl_str_mv Ghajari Espinosa, Adrián
Benito Santos, Alejandro
Ros Muñoz, Salvador
Fresno Fernández, Víctor Diego
González-Blanco García, Elena
author Ghajari Espinosa, Adrián
author_facet Ghajari Espinosa, Adrián
Benito Santos, Alejandro
Ros Muñoz, Salvador
Fresno Fernández, Víctor Diego
González-Blanco García, Elena
author_role author
author2 Benito Santos, Alejandro
Ros Muñoz, Salvador
Fresno Fernández, Víctor Diego
González-Blanco García, Elena
author2_role author
author
author
author
dc.contributor.none.fl_str_mv e-Spacio UNED
dc.subject.none.fl_str_mv 33 Ciencias Tecnológicas
compositional distributional semantics
semantic textual similarity
word embeddings
song lyrics
topic 33 Ciencias Tecnológicas
compositional distributional semantics
semantic textual similarity
word embeddings
song lyrics
description Song lyrics pose unique challenges for semantic similarity assessment due to their metaphorical language, structural patterns, and cultural nuances - characteristics that often challenge standard natural language processing (NLP) approaches. These challenges stem from a tension between compositional and distributional semantics: while lyrics follow compositional structures, their meaning depends heavily on context and interpretation. The Information Theory-based Compositional Distributional Semantics framework offers a principled approach by integrating information theory with compositional rules and distributional representations. We evaluate eight embedding models on Spanish song lyrics, including multilingual, monolingual contextual, and static embeddings. Results show that multilingual models consistently outperform monolingual alternatives, with the domain-adapted ALBERTI achieving the highest F1 macro scores (78.92 ± 10.86). Our analysis reveals that monolingual models generate highly anisotropic embedding spaces, significantly impacting performance with traditional metrics. The Information Contrast Model metric proves particularly effective, providing improvements up to 18.04 percentage points over cosine similarity. Additionally, composition functions maintaining longer accumulated vector norms consistently outperform standard averaging approaches. Our findings have important implications for NLP applications and challenge standard practices in similarity calculation, showing that effectiveness varies with both task nature and model characteristics.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-05-13
2025
2025-06-15
2025
2025-06-15
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/26536
url https://hdl.handle.net/20.500.14468/26536
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/deed.es
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by/4.0/deed.es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv ELSEVIER
publisher.none.fl_str_mv ELSEVIER
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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