Persona:
Fresno Fernández, Víctor Diego

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0000-0003-4270-2628
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Fresno Fernández
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Víctor Diego
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2018 - 201912020 - 20251
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search.filters.applied.f.itemFacultad: E.T.S. de Ingeniería Informática × Tipo: artículo ×

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Mostrando 1 - 2 de 2
  • 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
    Speech gestural interpretation by applying word representations in robotics
    (IOS Press, 2018-12-03) Almagro Cádiz, Mario; Paz López, Félix de la; Fresno Fernández, Víctor Diego
    Human-Robot Interaction (HRI) is a growing area of interest in Artificial Intelligence that aims to make interaction with robots more natural. In this sense, numerous research studies on verbal and visual interactions with robots have appeared. The present paper will focus on non-verbal communication and, more specifically, gestures related to speech, which is an open question. With the aim of developing this part of Human-Robot Interaction or HRI, a new architecture is proposed for the assignment of gestures to speech based on the analysis of semantic similarities. In this way, gestures will be intelligently selected using Natural Language Processing (NLP) techniques. The conditions for gesture selection will be determined from an assessment of the effectiveness of different language models in a lexical substitution task applied to gesture annotation. On the basis of this analysis, the aim is to compare models based on expert knowledge and statistical models generated from lexical learning.