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Ghajari Espinosa, Adrián

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Ghajari Espinosa
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Adrián
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2024 - 20253
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Mostrando 1 - 3 de 3
  • Miniatura
    Publicación
    Neural Approaches to Decode Semantic Similarities in Spanish Song Lyrics for Enhanced Recommendation Systems
    (Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos, 2024-02) Ghajari Espinosa, Adrián
    This dissertation explores the enhancement of music recommendation systems by integrating semantic similarity in Spanish song lyrics, utilizing advancements in machine learning and natural language processing (NLP), including both supervised and unsupervised approaches. It addresses the gap in current recommendation practices, which often overlook the rich semantic content of lyrics, despite their potential to significantly personalize music recommendations. Through theoretical insights into word embeddings and transfer learning, the development of the LyricSIM dataset for assessing lyric similarity, and empirical evaluations of models designed to distinguish between similar and non-similar song pairs, this research proposes a novel, lyrics-driven approach to music recommendation. Focused on the Spanish-speaking market, where Latin music is prevalent, this study contributes to the field by demonstrating how NLP technologies can refine music recommendations, addressing challenges like the cold start problem and enhancing the diversity of music recommendations, thereby offering a more personalized and engaging user experience in the streaming era.
  • Miniatura
    Publicación
    Test-driving information theory-based compositional distributional semantics: A case study on Spanish song lyrics
    (ELSEVIER, 2025-06-15) Ghajari Espinosa, Adrián; Benito Santos, Alejandro; Ros Muñoz, Salvador; Fresno Fernández, Víctor Diego; González Blanco, Elena
    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.
  • Miniatura
    Publicación
    Querying the depths: Unveiling the strengths and struggles of large language models in SPARQL generation
    (Sociedad Española para el procesamiento del lenguaje natural, 2024) Ghajari Espinosa, Adrián; Ros Muñoz, Salvador; Pérez Pozo, Álvaro
    The emergence of the Semantic Web has precipitated a proliferation of structured data manifested in the form of knowledge graphs, underscoring the imperative of natural language interfaces to enhance accessibility to these repositories of information. The capacity to articulate queries in natural language and subsequently retrieve data through SPARQL queries assumes paramount importance. In the present investigation, we have scrutinized the efficacy of in-context learning based on an agent-based architecture in facilitating the construction of SPARQL queries. Contrary to initial expectations, the augmentation of in-context learning prompts through agent-based mechanisms has been found to diminish the efficacy of Language Model-based Systems (LLMS), as it is perceived as extraneous "noise," thereby delineating the constraints inherent in this approach. The results highlight the need to delve deeper into the intricacies of model training and fine-tuning, focusing on the relational aspects of ontology schemas.