Author(s):
Kozhemyakina Olga Yurievna
PhD.
Position: Senior Research Scientist
Office: Federal Research Center for Information and Computational Technologies
Address: 630090, Russia, Novosibirsk, Academician M.A. Lavrentiev avenue, 6
Phone Office: (383) 330-78-26
E-mail: olgakozhemyakina@mail.ru

References:
1. Uspensky V.A. Introduction for readers of the “New Literary Observer” to the semiotic epistles of Andrei Nikolaevich Kolmogorov. New Literary Observer. 1997; (5):128. (In Russ.)

2. Analysis of poetic texts online. Available at: www.poem.ict.nsc.ru. (In Russ.)

3. Kozhemyakina O.Yu. Conceptual design of the software system for automated complex analysis of poetic texts. Computational Technologies. 2022; 27(2):122–137. DOI:10.25743/ICT.2022.27.2.010.

4. Magomedova D.M. Filologicheskiy analiz liricheskogo stikhotvoreniya [Philological analysis of a lyrical poem]. Moscow: Academiya; 2004: 187. (In Russ.)

5. Barakhnin V.B., Kozhemyakina O.Yu., Zabaykin A.V., Khayatova V.D. Automation of complex analysis of Russian poetic text: models and algorithms. Bulletin of NSU. Ser.: Information Technologies. 2015; 13(3):5–18. (In Russ.)

6. Barakhnin V.B., Fedotov A.M., Bakiev A.M., Bakiev M.N., Tazhibayeva S.Zh.,Batura T.V., Kozhemyakina O.Yu., Tusupov D.A., Sambetaeva M.A., Lukpanova L.H.Algorithms for generating stemmatization of word forms of the Kazakh language. Cloud of Science.2017; 4(3):434–449. (In Russ.)

7. Barakhnin V.B., Kozhemyakina O.Yu., Bakiуeva A.M., Sodboev M.K. The algorithms for complex analysis of the corpuses of poetic texts in the Kazakh language. Journal of Physics: Conference Series. 2018; (1117):7. DOI:10.1088/1742–6596/1117/1/012003.

8. Barakhnin V.B., Fedotov A.M., Bakiyeva A.M., Bakiyev M.N., Tazhibayeva S.Zh., Batura T.V., Kozhemyakina O.Yu., Tussupov D.A., Sambetbaiyeva M.A., Lukpanova L.Kh.The software system for the study the morphology of the Kazakh language. The European Proceedings
of Social and Behavioural Sciences. 2017; (XXXIII):18–27. DOI:10.15405/epsbs.2017.12.3.

9. Markov A.A. Example of statistical research on the text of “Eugene Onegin” illustrating the connection of tests in the chain. News of Imperial Academy of Sciences. 1913; 7(3):153–162. (In Russ.)

10. Kirillov I.A. O poeticheskoy informatsii. Informatsionnaya bezopasnost’ i mezhkul’turnaya kommunikatsiya v kontekste tsifrovoy transformatsii [On poetic information. Information security and intercultural communication in the context of digital transformation]. Moscow; 2022: 408. (In Russ.)

11. Kolmogorov v vospominaniyakh [Kolmogorov in memoirs]. Moscow: Fizmatlit; 1993: 736. (In Russ.)

12. Prokhorov A.V. O rabotakh A.N. Kolmogorova po stikhovedeniyu. Kolmogorov A.N. Trudy po stikhovedeniyu [About the works of A.N. Kolmogorov on poetry. Kolmogorov A.N. Works on poetry].Moscow: Izdatel’stvo MTsMNO; 2015: 256. (In Russ.)

13. Uspensky V.A. Introduction for readers of the “New Literary Observer” to the semiotic epistles of Andrei Nikolaevich Kolmogorov. New Literary Observer. 1997; (5):129. (In Russ.)

14. Kolmogorov A.N., Prokhorov A.V. K osnovam russkoy klassicheskoy metriki. Sodruzhestvo nauk i tayny tvorchestva [On the basics of Russian classical metrics. The Commonwealth of Sciences and the secrets of creativity]. Moscow: Iskusstvo; 1968: 397–432. (In Russ.)

15. Kolmogorov A.N. Trudy po stikhovedeniyu [Works on poetry]. Moscow: Izdatel’stvo MTsMNO;2015: 256. (In Russ.)

16. Gasparov M.L. Vladimir Mayakovskiy. Ocherki istorii yazyka russkoy poezii KhKh veka: opyty opisaniya idiostiley [Vladimir Mayakovsky. Essays on the history of the language of Russian poetry of the twentieth century: experiments describing idiostyles]. Moscow: Nasledie; 1995: 557. (In Russ.)

17. Gasparov M.L., Skulacheva T.V. Stat’i o lingvistike stikha [Articles on the linguistics of verse].Moscow: Yazyki slavyanskoy kul’tury; 2005: 132. (In Russ.)

18. Levin Yu.D. Concordance of Pushkin’s poetry. Russian Literature. 1987; (1):212–214. (In Russ.)

19. Shaw J.Th. Pushkin: poet and man of letters and his prose. L.A.; 1995: 273.

20. Shaw J.Th. Konkordans k stikham A.S. Pushkina. (A–N). T. 1. [Concordance to the poems of A.S. Pushkin. (A–N). Vol. 1]. Moscow: Yazyki russkoy kul’tury; 2000: 674. (In Russ.)

21. Shaw J.Th. Konkordans k stikham A.S. Pushkina. (O–Ya). T. 2. [Concordance to the poems of A.S. Pushkin. (O–Ya). Vol. 2]. Moscow: Yazyki russkoy kul’tury; 2000: 641. (In Russ.)

22. Kholshevnikov V.E. O slovare rifm Pushkina. Vremennik Pushkinskoy komissii [About the Dictionary of Pushkin’s rhymes. The Periodical of the Pushkin Commission]. 1973. Leningrad: Nauka; 1975: 135.(In Russ.)

23. Mittmann A. Escans˜ao autom´atica de versos em portuguˆes. Tese (doutorado). Universidade Federal de Santa Catarina, Centro Tecnol´ogico. Programa P´os–Gradua¸c˜ao em Ciˆencia da Computa¸c˜ao.Florian´opolis; 2016: 303. Available at: https://repositorio.ufsc.br/bitstream/handle/
123456789/175819/345411.pdf?sequence=1.

24. Pilshchikov I.A., Starostin A.S. Osnovnye problemy avtomatizatsii bazovykh protsedur ritmikosintaksicheskogo analiza sillabo-tonicheskikh tekstov. Natsional’nyy korpus russkogo yazyka: 2006–2008. Novye rezul’taty i perspektivy [The main problems of automation of basic procedures of rhythmic-syntactic analysis of syllabotonic texts. National corpus of the Russian language: 2006–2008. New results and perspectives]. St. Petersburg: Nestor-Istoriya; 2009: 502. (In Russ.)

25. Pilshchikov I.A., Starostin A.S. Avtomaticheskoe raspoznavanie metra: problemy i resheniya.Slavyanskiy stikh [Automatic meter recognition: problems and solutions. Slavic verse]. Moscow: Rukopisnye pamyatniki Drevney Rusi; 2012; (9):568. (In Russ.)

26. Pilshchikov I., Starostin A. Automated analysis of poetic texts and the problem of verse meter. Current trends in metrical analysis. Littera: Studies in Language and Literature. Berlin; 2011; (2):368.

27. Pilshchikov I., Starostin A. Reconnaissance automatique des m`etres des vers russes: une approche statistique sur corpus. Langages; 2015; 3(199):89–106.

28. Boikov N.V., Karyaeva M.S., Sokolov V.A., Pilshchikov I.A. On automatic verse specification in the information and analytical system. Proceedings of the XVII International Conference “Analytics and Data Management in Areas with Intensive Data Usage”. Obninsk: OINPE NRNU MEPhI;2015: 144–151. Available at: http://ceur-ws.org/Vol-1536/paper22.pdf. (In Russ.)

29. Barakhnin V.B., Kozhemyakina O.Yu. On automation of complex analysis of the Russian poetic text. CEUR Workshop Proceedings. 2012; (934):167–171. Available at: http://ceur-ws.org/Vol-934/paper27.pdf. (In Russ.)

30. Barakhnin V.B., Kozhemyakina O.Yu., Zabaykin A.V. Algorithms of complex analysis of Russian poetic texts in order to automate the process of creating metric reference books and concordances. CEUR Workshop Proceedings. 2015; (1536):138–143. Available at: http://ceur-ws.org/Vol-1536/paper21.pdf. (In Russ.)

31. MyStem. Available at: https://tech.yandex.ru/mystem.

32. Rifmofed.ru. All about rhyme and versification. Available at: http://rifmoved.ru.

33. Mansilla R., Bush E. Increase of complexity from classical Greek to Latin poetry. Complex Systems.2003; 14(3). Available at: https://arxiv.org/pdf/cond-mat/0203135.pdf.

34. R´enyi A. On measures of information and entropy. Proceedings of the 4th Berkeley Symposium on Mathematics, Statistics and Probability. 1960: 547–561.

35. Yarkho V.N. Gomerovskiy vopros. Literaturnaya entsiklopediya [The Homeric question. Literary encyclopedia]. Moscow: Sovetskaya Entsiklopediya; 1987: 78. (In Russ.)

36. Fusi D. A multilanguage, modular framework for metrical analysis: IT patterns and theorical issues.Langages. 2015; 3(199):41–66.

37. Fusi D. An expert system for the classical languages: metrical analysis components. Lexis. 2008;(27):25–45.

38. Mishina Yu.E. Appozitivnye konstruktsii i smezhnye sintaksicheskie yavleniya angliyskogo yazyka:problema razgranicheniya [Appositive constructions and related syntactic phenomenas of the English language: the problem of differentiation]. Filologicheskie Nauki. Voprosy Teorii i Praktiki. Tambov:Gramota; 2018; 1(1):155–158. DOI:10.30853/filnauki.2018-1-1.40. (In Russ.)

39. Foley J.M. A computer analysis of metrical patterns in Beowulf. Computers and the Humanities.1978; (12):71–80.

40. Hidley G.R. Some thoughts concerning the application of software tools in support of old English poetic studies. Literary and Linguistic Computing. 1986; 1(3):156–162.

41. Barquist C.R., Shie D.L. Computer analysis of alliteration in Beowulf using distinctive feature theory. Literary and Linguistic Computing. 1991; 6(4):27–280.

42. Donow H.S. Prosody and the computer. A text processor for stylistic analysis. Spring Joint Computer Conference. 1970: 712.

43. Barber C., Barber N. The versification of The Canterbury Tales: a computer-based statistical study. Pt I. Leeds Studies in English. 1990; (21):81–103.

44. Barber C., Barber N. The versification of The Canterbury Tales: a computer-based statistical study. Pt II. Leeds Studies in English. 1991; (22):57–83.

45. Greene E., Bodrumlu T., Knight K. Automatic analysis of rhythmic poetry with applications to generation and translation. Conference on Empirical Methods in Natural Language Processing.2010: 524–533.

46. Hayward M. A connectionist model of poetic meter. Poetics. 1991; (20):303–317.

47. Hayward M. Analysis of a corpus of poetry by a connectionist model of poetic meter. Poetics. 1996;24(1):1–11. Available at: http://www.english.iup.edu/mhayward/Metrics/Cormetrics.htm.

48. Plamondon M.R. Computer-assisted phonetic analysis of English poetry: a preliminary case study of Browning and Tennyson. Text Technology. 2005; 14(2):153–175.

49. Plamondon M.R. Virtual verse analysis: analysing patterns in poetry. Literary and Linguistic Computing. 2006; 21(1):127–141.

50. Hartman C. The Scandroid. New London; 2005. Available at: http://charlesohartman.com/verse/scandroid/ScandroidManual.pdf.

51. The Scandroid. Available at: http://charlesohartman.com/verse/scandroid/index.php.

52. Tizhoosh H.R., Dara R.A. On poem recognition. Pattern Analysis and Applications. 2006;(9):325–338.

53. Kaplan D.M., Blei D.M. A computational approach to style in American poetry. 7th IEEE International Conference on Data Mining (ICDM 2007). 2007: 553–558.

54. Kao J., Jurafsky D. A computational analysis of style, affect, and imagery in contemporary poetry.NAACL Workshop on Computational Linguistics for Literature. 2012. Available at: https://nlp.stanford.edu/pubs/kaojurafsky12.pdf.

55. Kavanagh F. Analysis of a phonetic and rule based algorithm approach to determine rhyme categories and patterns in verse. Diss. (Mestrado). Open University; 2007.

56. Genzel D., Uszkoreit J., Och F. “Poetic” statistical machine translation: rhyme and meter.Conference on Empirical Methods in Natural Language Processing. 2010: 158–166.

57. Hirjee H. Rhyme, rhythm, and rhubarb: using probabilistic methods to analyze hip hop, poetry,and misheard lyrics. University of Waterloo. 2010. Available at: https://uwspace.uwaterloo.ca/bitstream/handle/10012/5419/Hirjee_Hussein.pdf.

58. Agirrezabal M., Arrieta B., Astigarraga A., Hulden M. ZeuScansion: a tool for scansion of English poetry. 11th International Conference on Finite State Methods and Natural Language Processing. The Gateway, St Andrews, Scotland (UK), July 15–17, 2013. 2013: 18–24.

59. Delmonte R. Computing poetry style. CEUR Workshop Proceedngs. 2013; (1096):148–155. Availableat: http://ceur-ws.org/Vol1096/paper11.pdf.

60. SPARSAR. Available at: https://sparsar.wordpress.com.

61. Delmonte R., Tonelli S., Boniforti M.A.P., Bristot A., Pianta E. VENSES — a linguisticallybased system for semantic evaluation. Machine Learning Challenges. Evaluating Predictive Uncertainty,Visual Object Classification, and Recognising Tectual Entailment. 2005: 344–371.
DOI:10.1007/11736790_20. Available at: https://www.researchgate.net/publication/225240840_VENSES_-_A_Linguistically Based_System_for_Semantic_Evaluation.

62. Bacalu C., Delmonte R. Prosodic modeling for speech recognition. Atti del Workshop AI*IA,“Elab.Ling.e Ric.”. IRST Trento; 1999: 45–55.

63. McCurdy N., Srikumar V., Meyer M. RhymeDesign: a tool for analyzing sonic devices in poetry.4th Workshop on Computational Linguistics for Literature. 2015: 12–22. Available at: https://sci.utah.edu/~vdl/papers/2015_clfl_rhymedesign.pdf.

64. McCurdy N., Lein J., Coles K., Meyer M. Poemage: visualizing the sonic topology of a poem.IEEE Transactions on Visualization and Computer Graphics. 2016; 22(1):439–448.

65. Calin O. Statistics and machine learning experiments on English and Romanian poetry. Applied Sciences. 2020; 2(4):92. DOI:10.3390/sci2040092.

66. Shannon C.E. A mathematical theory of communication. Bell System Technical Journal. 1948;27(3):379–423.

67. Chishlom D. Phonology and style: a computer-assisted approach to German verse. Computers and the Humanities. 1981; (15):199–210.

68. Metricalizer2. Available at: https://metricalizer.de.

69. Bobenhausen K., Hammerich K. M´etrique litt´eraire, m´etrique linguistique et m´etrique algorithmique de l’allemand mises en jeu dans le programme Metricalizer2. Traitement automatique des textes versifi´es: probl´ematiques et pratiques. Languages. 2015; 199(3):67–87.

70. Freiburger anthologie. Textgrid. Available at: https://metricalizer.de/en/about.

71. Wells J.C. Computer-coding the IPA: a proposed extension of SAMPA. Available at: https://www.phon.ucl.ac.uk/home/sampa/ipasam-x.pdf.

72. Estes A., Hench C. Supervised machine learning for hybrid meter. 5th Workshop on Computational Linguistics for Literature. 2016: 1–8. Available at: https://aclanthology.org/W16-0201.pdf.

73. Gerv´as P. A logic programming application for the analysis of Spanish verse. 1st International Conference on Computational Logic. London, UK, July 24–28, 2000: 1399. Available at: https://link.springer.com/chapter/10.1007/3-540-44957-4_89.

74. Ara´ujo P.A., Mamede N.J. Classificador de poemas. Conferˆencia Cientif´ıca e Tecnol´ogica em Engenharia. Lisboa, Portugal, 2002.

75. Ara´ujo P.A.M. Classifica¸c˜ao de poemas e sugest˜ao das palavras finais dos versos. Diss. (Mestrado).Universidade T´ecnica de Lisboa; 2004.

76. Mamede N., Trancoso I., Arau´ujo P., Viana C. Poetry assistant. Proceedings of the 8th International Conference on Spoken Language Processing. 2004: 3088.

77. Mamede N., Trancoso I., Ara´ujo P., Viana C. An electronic assistant for poetry writing. Advances in Artificial Intelligence — IBERAMIA 2004, 9th Ibero-American Conference on AI. Puebla, M´exico, 2004: 286–294. DOI:10.1007/978-3-540-30498-2_29. Available at: https://www.researchgate.net/publication/220943156_An_Electronic_Assistant_for_Poetry_Writing.

78. Marques J.A.D. Sistema de apoio ´a escrita de poemas. Diss. Universidade T´ecnica de Lisboa;2008: 89.

79. Oliveira L.C., Viana M.C., Trancoso I.M. A rule-based text-to-speech system for Portuguese.International Conference on Acoustics, Speech, and Signal Processing. 1992; (2):73–76. Available at:https://ieeexplore.ieee.org/document/226117.

80. Robinson J.R. Colors of poetry: computational deconstruction. Georgia State University; 2006.Available at: https://getd.libs.uga.edu/pdfs/robinson_jason_r_200605_ma.pdf.

81. Navarro-Colorado B. A computational linguistic approach to Spanish Golden Age sonnets: metrical and semantic aspects. Fourth Workshop on Computational Linguistics for Literature. USA: Denver;2015: 105–113. DOI:10.3115/v1/W15-0712. Available at: https://www.researchgate.net/
publication/316284856_A_computational_linguistic_approach_to_Spanish_Golden_Age_Sonnets_metrical_and_semantic_aspects.

82. Navarro-Colorado B., Lafoz M.R., S´anchez N. Metrical annotation of a large corpus of Spanish sonnets: representation, scansion and evaluation. 9th International Conference on Language Resources and Evaluation. 2016: 5. Available at: http://www.lrec-conf.org/proceedings/lrec2016/pdf/
453_Paper.pdf.

83. Navarro-Colorado B., Lafoz M.R., Trigueros S.J., S´anchez N. Compilaci´on y anotaci´on m´etrica de un corpus de sonetos del Siglo de Oro. II Congreso Internacional Humanidades Digitales Hisp´anicas: “Innovaci´on, Globalizaci´o e Impacto”. Madrid, Espa˜na, 5–7 Octobre, 2015. Available at:https://hispanismo.cervantes.es/congresos-y-cursos/ii-congreso-internacionalhumanidades-digitales-hispanicas-innovacion-0.

84. Text encoding initiative. Available at: https://tei-c.org.

85. Robey D. Scanning Dante’s the Divine Comedy. A computer-based approach. Literary and Linguistic Computing. 1993; 8(2):81–84.

86. Rainsford T.M., Scrivner O. Metrical annotation for a verse treebank. The 13th International Workshop on Treebanks and Linguistic Theories (TLT13). Germany: T¨ubungen; 2014: 149–159. Available at: https://www.researchgate.net/publication/269410991_Metrical_Annotation_For_a_
Verse_Treebank_wwwoldoccitancorpusorg.

87. Roubaud J. DYNASTIE: ´etudes sur le vers Fran¸cais, sur l’alexandrin classique. Cahiers de po´etique compar´ee. Premi´ere Partie. 1986; (13):47–109.

88. Roubaud J. DYNASTIE: ´etudes sur le vers Fran¸cais, sur l’alexandrin classique. Cahiers de po´etique compar´ee. Deuxi´eme Partie. 1988; (16):41–60.

89. Beaudouin V., Yvon F. The Metrometer: a tool for analysing French verse. Literary and Linguistic Computing. 1996; 11(1):23–31.

90. Beaudouin V. M´etre en r´egles. Revue Fran¸caise de Linguistique Appliqu´ee. 2004; IX(1):119–137.DOI:10.3917/rfla.091.0119. Available at: https://www.researchgate.net/publication/268150694_Metre_en_regles.

91. Delente E., Renault R. ´ Annotation automatique des textes versifi´es. Schedae. 2011: 39–52.

92. Delente E., Renault R. ´ Projet anam´etre: le calcul du m´etre des vers complexes. Langages. 2015;3(199):125–148. Available at: https://www.cairn.info/revue-langages-2015-3-page-125.htm&wt.src=pdf.

93. Delente E., Renault R. ´ Traitement automatique des formes m´etriques des textes versifi´es. Actes de la 22e Conf´erence sur le Traitement Automatique des Langues Naturelles. Caen, France.ATALA2015:116–122. Available at: https://aclanthology.org/2015.jeptalnrecital-court.18.pdf.

94. Ayech H.E., Mahfouf A., Zribi A. Reconnaissance de la m´etrique des po´emes arabes par les r´eseaux de neurones artificiels. 13´eme Conf´erence sur le Traitement Automatique des Langues Naturelles.2006: 462–472. Available at: https://aclanthology.org/2006.jeptalnrecital-poster.10.pdf.

95. Kouloughli D.E. Traitement automatique de la m´etrique arabe: r´ealisations et perspectives. Bulletin D’´etudes Orientales. 2010; (LIX):17–31. Available at: https://www.cairn.info/revue-bulletin-detudes-orientales-2010-1-page-17.htm.

96. Almuhareb A., Alkharashi I., Saud L.AL., Altuwaijri H. Recognition of classical Arabic poems.Proceedings of the Second Workshop on Computational Linguistics for Literature. Atlanta, Georgia,June 14, 2013. 2013: 9–16. Available at: https://aclanthology.org/W13-1402.pdf.

97. Kurt A., Kara M. An algorithm for the detection and analysis of arud meter in Diwan poetry.Turkish Journal of Electrical Engineering and Computer Sciences. 2012; 20(6):948–963.

98. Alnagdawi M.A., Rashideh H., Aburumman A.F. Finding Arabic poem meter using context free grammar. Journal of Communications and Computer Engineering. 2013; 3(1):52–59.

99. Wujastyk D. Automatic scansion of sanskrit poetry for authorship criteria. Association for Literary and Linguistic Computing Bulletin. 1978; 6(2):122–135.

100. Mayrhofer C.M. Scansion and analysis of Prakrit verses by text-processing programs. Revue Informatique et Statistique dans les Sciences Humaines. 1987; (XXIII):99–110.

101. Ousaka Y.M., Yamazaki M.M. Automatic analysis of the Canon in Middle Indo-Aryan by personal computer. Literary and Linguistic Computing. 1994; 9(2):125–136.

102. Ousaka Y.M., Yamazaki M.M. Automatic analysis of the Canon in Middle Indo-Aryan by personal computer II. Literary and Linguistic Computing. 1996; 11(1):9–17.

103. Rama N., Lakshmanan M. A computational algorithm for metrical classification of verse. International Journal of Computer Science Issues. 2010; 7(2):46–53.

104. Rakshit G., Ghosh A., Bhattacharyya P., Haffari G. Automated analysis of Bangla poetry for classification and poet identification. 12th International Conference on Natural Language Processing.2015: 247–253. Available at: https://aclanthology.org/W15-5937.pdf.

105. Sgallov´a K. Vyuˇzit´ı modern´ı techniky pˇri rozboru verˇse. Cesk´a Literatura. 1964; 12(2):158–168. ˇ

106. Sgallov´a K. Thesaurus ˇcesk´ych meter. Cesk´a Literatura. 1999; 47(3):286–289. ˇ

107. Ibrahim R., Plech´aˇc P. Towards the automatic analysis of Czech verse. Formal methods in poetics.L¨udenscheid: RAM-Verlag; 2011: 295–305.

108. Plech´aˇc P. Czech verse processing system KVETA — phonetic and metrical components. ˇ Glottotheo- ˇry. 2016; 7(7):159–174.

109. Plech´aˇc P., Kol´ar R. The corpus of Czech verse. Studia Metrica et Poetica. 2015; 2(1):107-118.

110. Gasparov M.L. Evolyutsiya russkoy rifmy. Problemy teorii stikha [Evolution of Russian rhyme.Problems of the theory of verse]. Leningrad: Nauka; 1984: 255. (In Russ.)

111. Samoylov D. Kniga o russkoy rifme [The book on Russian rhyme]. Moscow: Khudozhestvennaya Literatura; 1982: 351. (In Russ.)

112. Breido E.M. Avtomaticheskiy analiz metriki russkogo stikha [Russian verse metric automatic analysis]. Avtoreferat dissertatsii po filologii. Moscow: Institut Russkogo Yazyka RAN im. V.V. Vshyugradova; 1996: 26. (In Russ.)

113. Breido E.M. Interval model of Russian metrics. Questions of Linguistics. 1996; (4):85–94. (In Russ.)

114. Breido E.M. Interval model of Russian metrics and strict tonic verse. Questions of Linguistics. 2021;(5):106–136. (In Russ.)

115. Polyakov A.E., Pilshchikov I.A., Bergelson M.B. Konkordans k tekstam Lomonosova [Concordance to the texts of Lomonosov]. FEB; 2009. Available at: http://feb-web.ru/feb/lomoconc/abc.(In Russ.)

116. Polyakov A.E., Pilshchikov I.A., Bergelson M.B. Konkordans k tekstam Lomonosova — kontseptsiya i realizatsiya [Lomonosov concordance — concept and implementation]. Available at: https://www.dialog21.ru/digests/dialog2009/materials/html/61.htm. (In Russ.)

117. Elektronnoe nauchnoe izdanie “Lomonosov” [Electronic scientific publication “Lomonosov”]. Available at: http://febweb.ru/feb/lomonos/default.asp. (In Russ.)

118. Vavilonskaya Bashnya. Proekt etimologicheskoy bazy dannykh. Russkie slovari i morfologiya [The Tower of Babel. Etymological database project. Russian dictionaries and morphology]. Available at:https://starlingdb.org/main.html. (In Russ.)

119. Krylov S.A., Starostin S.A. Aktual’nye zadachi morfologicheskogo analiza i sinteza v integrirovannoy informatsionnoy srede STARLING [Actual problems of morphological analysis and synthesis in the integrated information environment of STARLING]. International Conference “Dialogue”: Computational Linguistics and Intelligent Technologies. Archive. 2003. Available at: https://www.dialog-21.ru/media/2655/krylov.pdf.

120. Pilshchikov I.A., Starostin A.S. Avtomaticheskoe raspoznavanie stikhotvornykh razmerov: teoriya i praktika [Automatic recognition of poetic dimensions: theory and practice]. Poetics and Phonostylistics: Brik’s Collection. Is. 1. Proceedings of the International Scientific Conference “I-st Briks’ readings:Poetics and Phonostylistics”. Moscow; 2010: 41–49. (In Russ.)

121. Pilshchikov I.A., Starostin A.S. Problema avtomaticheskogo raspoznavaniya metra: sillabotonika,dol’nik, taktovik [The problem of automatic meter recognition: syllabotonics, dolnik, taktovik]. Russian Poetry: 100-year Results and Prospects of Development. Materials of the International Scientific Conference. November 25–27, 2010. St. Petersburg; 2010: 397–406. (In Russ.)

122. Boykov V.N., Zakharov V.E., Karyaeva M.S., Sokolov V.A. Thesaurus on poetologie as a tool for information retrieval and knowledge collection. Modeling and Analysis of Information Systems. 2013; 20(4):125–135. Available at: http://www.mathnet.ru/php/archive.phtml?wshow=
paper&jrnid=mais&paperid=327&option_lang=eng. (In Russ.)

123. Drozdova I.I., Obukhova A.D. Opredelenie avtorstva teksta po chastotnym kharakteristikam [Determining the authorship of the text by frequency characteristics]. Proceedings of the VII International Scientific Conference “Technical Sciences in Russia and Abroad”. Moscow: Buki-Vedi; 2017: 18–21.(In Russ.)

124. Grechnikov E.A., Gusev G.G., Kustarev A.A., Raygorodsky A.M. Poisk neestestvennykh tekstov [Search for unnatural texts]. Proceedings of the XXI All-Russian Scientific Conference “Electronic Libraries: Perspective Methods and Technologies, Electronic Collections”. Petrozavodsk: Transkript;2009: 306–308. (In Russ.)

125. Barakhnin V.B., Kozhemyakina O.Yu., Zabaykin A.V. Tekhnologiya sozdaniya metricheskikh spravochnikov i konkordansov russkikh poeticheskikh tekstov [Technology of creating metric reference books and concordances of Russian poetic texts]. Proceedings of the International Conference “Computing and Information Technologies in Science, Technology and Education”. Alma-Ata; 2015: 244–245.(In Russ.)

126. Barakhnin V., Kozhemyakina O., Grigorieva I.V. Determination of the features of the author’s style of A.S. Pushkin’s poems by machine learning methods. Applied Sciences. 2022; (12):1674.DOI:10.3390/app12031674.

127. Kozhemyakina O.Yu. Programmnaya sistema kompleksnogo analiza russkikh poeticheskikh tekstov: modeli i algoritmy [Software system for complex analysis of Russian poetic texts: models and algorithms]. Dis. ... Doctor of Technical Sciences: 05.13.17 — Theoretical Foundations of Computer Science.Novosibirsk; 2022: 288. (In Russ.)