Understanding poetry using natural language processing tools: a survey.

Analyzing poetry with automatic tools has great potential for improving verse-related research. Over the last few decades, this field has expanded notably and a large number of tools aiming at analyzing various aspects of poetry have been developed. However, the concrete connection between these too...

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Publicado en:Digital Scholarship in the Humanities Vol. 39; no. 2; pp. 500 - 522
Autores principales: Sisto, Mirella De, Hernández-Lorenzo, Laura, Rosa, Javier De la, Ros, Salvador, González-Blanco, Elena
Formato: Artículo
Publicado: Oxford University Press / USA Jun2024
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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          Sisto, Mirella De
          Hernández-Lorenzo, Laura
          Rosa, Javier De la
          Ros, Salvador
          González-Blanco, Elena
        affil:
          Tilburg University , The Netherlands
          National Distance Education University , Spain
          National Library of Norway , Norway
          IE University , Spain
      su:
        Natural language processing
        Poetry (Literary form)
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          Natural language processing
          Poetry (Literary form)
      keyword:
        Computational Literary Studies
        Natural Language Processing
        Poetry analysis
      ab: Analyzing poetry with automatic tools has great potential for improving verse-related research. Over the last few decades, this field has expanded notably and a large number of tools aiming at analyzing various aspects of poetry have been developed. However, the concrete connection between these tools and traditional scholars investigating poetry and metrics is often missing. The purpose of this article is to bridge this gap by providing a comprehensive survey of the automatic poetry analysis tools available for European languages. The tools are described and classified according to the language for which they are primarily developed, and to their functionalities and purpose. Particular attention is given to those that have open-source code or provide an online version with the same functionality. Combining more traditional research with these tools has clear advantages: it provides the opportunity to address theoretical questions with the support of large amounts of data; also, it allows for the development of new and diversified approaches.
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