Persona: Pérez Pozo, Álvaro
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Publicación The Automatic Quantitative Metrical Analysis of Spanish Poetry with Rantanplan: A Preliminary Approach(ICL CAS, 2021) Hernández Lorenzo, Laura; Sisto, Mirella De; Pérez Pozo, Álvaro; Rosa, Javier de la; Ros Muñoz, Salvador; González Blanco, Elena; Plecháč, P.; Kolár, R.; Bories,A.; Říha, J.In this paper, we present a quantitative approach to Spanish poetry and versification based on the application of our own automatic metrical tool, Rantanplan, to the complete poetic works of four early modern Spanish poets. All of the poetry of these four representative authors—Garcilaso de la Vega (1503–1536), Fernando de Herrera (1534–1597), Luis de Góngora (1561–1627), and Lope de Vega (1562–1635)—was automatically processed and stress positions were extracted. Thanks to the development of a new stanza identification feature of Rantanplan, we were able to detect metrical structures as well. By completing a quantitative analysis of the stress positions, line lengths, and stanzas used by each author, we aim to model their complete metrical profiles.Publicación Transformers analyzing poetry: multilingual metrical pattern prediction with transfomer-based language models(Springer, 2023) Rosa, Javier de la; Pérez Pozo, Álvaro; Sisto, Mirella De; Hernández Lorenzo, Laura; Díaz Paredes, Aitor; Ros Muñoz, Salvador; González Blanco, ElenaThe splitting of words into stressed and unstressed syllables is the foundation for the scansion of poetry, a process that aims at determining the metrical pattern of a line of verse within a poem. Intricate language rules and their exceptions, as well as poetic licenses exerted by the authors, make calculating these patterns a nontrivial task. Some rhetorical devices shrink the metrical length, while others might extend it. This opens the door for interpretation and further complicates the creation of automated scansion algorithms useful for automatically analyzing corpora on a distant reading fashion. In this paper, we compare the automated metrical pattern identification systems available for Spanish, English, and German, against fine-tuned monolingual and multilingual language models trained on the same task. Despite being initially conceived as models suitable for semantic tasks, our results suggest that transformers-based models retain enough structural information to perform reasonably well for Spanish on a monolingual setting, and outperforms both for English and German when using a model trained on the three languages, showing evidence of the benefits of cross-lingual transfer between the languages.Publicación Rantanplan, Fast and Accurate Syllabification and Scansion of Spanish Poetry(Sociedad Española para el Procesamiento del Lenguaje Natural, 2020-09) Rosa, Javier de la; Pérez Pozo, Álvaro; Hernández Lorenzo, Laura; Ros Muñoz, Salvador; González Blanco, ElenaAutomated analysis of Spanish poetry corpora lacks the richness of tools available for English. The existing options suffer from a number of issues: are limited to fixed-metre hendecasyllabic verses, are not publicly available, the syllabification procedure underneath is not thoroughly tested, and their speed is questionable. This paper introduces new methods to alleviate these concerns. For syllabification, we contribute with our own method and manually crafted corpus. For scansion, our approach is based on a heuristic for the application of rhetorical figures that alter metrical length. Experimental evaluation shows that both fixed-metre and mixed-metre poetry can be successfully analyzed, producing metrical patterns more accurately (increasing accuracy by 2% and 15%, respectively), and at a fraction of the time other methods need (running at least 100 times faster).Publicación A bridge too far for artificial intelligence?: Automatic classification of stanzas in Spanish poetry(Wiley, 2022) Pérez Pozo, Álvaro; Rosa, Javier de la; Ros Muñoz, Salvador; González Blanco, Elena; Hernández Lorenzo, Laura; Sisto, Mirella deThe rise in artificial intelligence and natural language processing techniques has increased considerably in the last few decades. Historically, the focus has been primarily on texts expressed in prose form, leaving mostly aside figurative or poetic expressions of language due to their rich semantics and syntactic complexity. The creation and analysis of poetry have been commonly carried out by hand, with a few computer-assisted approaches. In the Spanish context, the promise of machine learning is starting to pan out in specific tasks such as metrical annotation and syllabification. However, there is a task that remains unexplored and underdeveloped: stanza classification. This classification of the inner structures of verses in which a poem is built upon is an especially relevant task for poetry studies since it complements the structural information of a poem. In this work, we analyzed different computational approaches to stanza classification in the Spanish poetic tradition. These approaches show that this task continues to be hard for computers systems, both based on classical machine learning approaches as well as statistical language models and cannot compete with traditional computational paradigms based on the knowledge of experts.