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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 - 20253
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Mostrando 1 - 4 de 4
  • 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.
  • 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, 2024-05-15) Ghajari Espinosa, Adrián; Ros Muñoz, Salvador; Pérez Pozo, Álvaro; Fresno Fernández, Víctor Diego; SEPLN, Sociedad Española para el Procesamiento del lenguaje natural
    In the quest to democratize access to databases and knowledge graphs, the ability to express queries in natural language and obtain the requested information becomes paramount, particularly for individuals lacking formal training in query languages. This situation affects SPARQL, the standard for querying ontology-based knowledge graphs, posing a significant barrier to many, hindering their ability to leverage these rich resources for research and analysis. To address this gap, our research delves into harnessing the power of Large Language Models (LLMs) to facilitate the generation of SPARQL queries directly from natural language descriptions. For this purpose, we have explored the most popular prompt engineering techniques, a powerful tool in crafting queries that help generative AI models understand and produce specific or generalized outputs based on the quality of provided prompts, without the need of aditional training. By integrating few-shot learning (FSL), Chain-of-Thought (CoT) reasoning, and Retrieval-Augmented Generation (RAG), we devise prompts that streamline the creation of effective SPARQL queries, facilitating more straightforward access to ontology knowledge graphs. Our analysis involved prompts evaluated across three distinct LLMs: DeepSeek-Code 6.7b, CodeLlama-13b and GPT 3.5 TURBO. The comparative results revealed marginal variations in accuracy among these models, with FSL emerging as the most effective technique. Our results highlight the potential of LLMs to make knowledge graphs more accessible to a broader audience, but also that much more research is needed to get results comparable to human performance.
  • Miniatura
    Publicación
    DISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labels
    (Springer, 2021-10-13) Barbado, Alberto; Fresno Fernández, Víctor Diego; Manjarrés Riesco, Ángeles; Ros Muñoz, Salvador
    Nowadays, there are many applications of text mining over corpora from different languages. However, most of them are based on texts in prose, lacking applications that work with poetry texts. An example of an application of text mining in poetry is the usage of features derived from their individual words in order to capture the lexical, sublexical and interlexical meaning, and infer the General Affective Meaning (GAM) of the text. However, even though this proposal has been proved as useful for poetry in some languages, there is a lack of studies for both Spanish poetry and for highly-structured poetic compositions such as sonnets. This article presents a study over an annotated corpus of Spanish sonnets, in order to analyse if it is possible to build features from their individual words for predicting their GAM. The purpose of this is to model sonnets at an affective level. The article also analyses the relationship between the GAM of the sonnets and the content itself. For this, we consider the content from a psychological perspective, dentifying with tags when a sonnet is related to a specific term. Then, we study how GAM changes according to each of those psychological terms. The corpus used contains 274 Spanish sonnets from authors of different centuries, from fifteenth to nineteenth. This corpus was annotated by different domain experts. The experts annotated the poems with affective and lexico-semantic features, as well as with domain concepts that belong to psychology. Thanks to this, the corpus of sonnets can be used in different applications, such as poetry recommender systems, per- sonality text mining studies of the authors, or the usage of poetry for therapeutic purposes.