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Poetic Discourse Processing Applying Graph Theoretic Rules

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Advances in Intelligent Systems and Computing V (CSIT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1293))

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Abstract

In this article, we present a way of graph theory application to analyze the structure and content of poetic discourse. The study has been carried out in terms of applied linguistics and illustrates the integration of information systems technologies. The main contribution is an attempt to implement the open-source text analytics software to analyze the poetic discourse of the late 16th ‒ early 17th centuries and provide its infographics visualization. The topicality of graph theory and data visualization applications in linguistic studies are connected with the ability to structure the retrieved information, identify the contextual relations between the key phrases and language units, and make predictions regarding the dynamics of discourse organization. Overall, the research findings indicate the most frequent connections between the lingual cultural type of woman and the lexical variables used to depict physical, social, or spiritual objects of the reality in English culture.

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Authors and Affiliations

  1. Applied Linguistics Department, Lviv Polytechnic National University, Lviv, Ukraine

    Olena Flys & Maria Bekhta-Hamanchuk

Authors
  1. Olena Flys
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  2. Maria Bekhta-Hamanchuk
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Correspondence to Olena Flys .

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Editors and Affiliations

  1. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Natalya Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

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Flys, O., Bekhta-Hamanchuk, M. (2021). Poetic Discourse Processing Applying Graph Theoretic Rules. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_3

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