Persona: Díaz Paredes, Aitor
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Díaz Paredes
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Publicación PoetryLab as Infrastructure for the Analysis of Spanish Poetry(Linköping University Electronic Press, 2021-06-22) Rosa, Javier de la; Pérez Pozo, Álvaro; Hernández Lorenzo, Laura; Díaz Paredes, Aitor; Ros Muñoz, Salvador; González Blanco, ElenaThe development of the network of ontologies of the ERC POSTDATA Project brought to light some deficiencies in terms of completeness in the currently available European poetry corpora. To tackle the issue in the realm of the Spanish poetic tradition, our approach consisted in designing a set of tools that any scholar could use to automatically enrich the analysis of Spanish poetry. The effort crystallized in the PoetryLab, an extensible open source toolkit for syllabification, scansion, enjambment detection, rhyme detection, stanza identification, and historical named entity recognition for Spanish poetry. We designed the system to be interoperable, compliant with the project ontologies, easy to use by tech-savvy and non-expert researchers, and requiring minimal maintenance and setup. Furthermore, we propose the integration of the PoetryLab as a core functionality in the tool catalog of CLARIN for Spanish poetry.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.