Publicación: Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics
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2025-06-15
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info:eu-repo/semantics/openAccess
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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.
Descripción
The registered version of this article, first published in “Knowledge-Based Systems, vol. 319, 2025", is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.knosys.2025.113549
La versión registrada de este artículo, publicado por primera vez en “Knowledge-Based Systems, vol. 319, 2025", está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.knosys.2025.113549
Categorías UNESCO
Palabras clave
compositional distributional semantics, semantic textual similarity, word embeddings, song lyrics
Citación
Adrián Ghajari, Alejandro Benito-Santos, Salvador Ros, Víctor Fresno, Elena González-Blanco, Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics, Knowledge-Based Systems, Volume 319, 2025, 113549, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2025.113549
Centro
E.T.S. de Ingeniería Informática
Departamento
Lenguajes y Sistemas Informáticos