Artificial Intelligence and Decision Making
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Artificial Intelligence and Decision Making, 2025, Issue 1, Pages 95–102
DOI: https://doi.org/10.14357/20718594250108 (Mi iipr620) 		 
Analysis of textual and graphical information

The impact of hierarchical discourse features on coreference resolution in Russian

E. V. Chistova

Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
Full-text PDF (384 kB)
DOI: https://doi.org/10.14357/20718594250108
Abstract: This study investigates the role of hierarchical discourse features in coreference resolution within Russian texts. It evaluates the effectiveness of rhetorical parsers in handling coreference across texts of varying genres and lengths. The paper also identifies key characteristics of rhetorical structure annotation corpora that influence the quality of coreference resolution in diverse linguistic contexts.
Keywords: rhetorical structure theory, deep learning, Russian language, coreference resolution.
Funding agency Grant number
Ministry of Science and Higher Education of the Russian Federation 075-03-2024-490/2
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: E. V. Chistova, “The impact of hierarchical discourse features on coreference resolution in Russian”, Artificial Intelligence and Decision Making, 2025, no. 1, 95–102
Citation in format AMSBIB
\Bibitem{Chi25}
\by E.~V.~Chistova
\paper The impact of hierarchical discourse features on coreference resolution in Russian
\jour Artificial Intelligence and Decision Making
\yr 2025
\issue 1
\pages 95--102
\mathnet{http://mi.mathnet.ru/iipr620}
\crossref{https://doi.org/10.14357/20718594250108}
\elib{https://elibrary.ru/item.asp?id=80493704}
Linking options:
  • https://www.mathnet.ru/eng/iipr620
  • https://www.mathnet.ru/eng/iipr/y2025/i1/p95
    • Citing articles in Google Scholar: Russian citations, English citations
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    		Artificial Intelligence and Decision Making
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