THE USE OF SEMANTIC INFORMATION TO DISAMBIGUATE THE NOMINATIVE/ACCUSATIVE HOMONYMS: AN ELEMENT OF CREATING ONTOLOGY

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Shelepov Vladislav Yurievich
Doctor of Physical and Mathematical Sciences, Professor, Head of the Department of Artificial Intelligence Systems, Institute of Informatics and Artificial Intelligence, Donetsk National Technical University.
283048, Donetsk, Artyoma str., 118-b.
Research interests: speech pattern recognition, artificial intelligence systems.

Nitsenko Artyom Vladimirovich
Professional programmer.
283048, Donetsk, Artyoma str., 118-b.
Research interests: speech recognition.

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UDC 004.89:004.93
DOI 10.24412/2413-7383-2024-4-16-24
Language: Russian
Annotation: The article proposes a method for automatic disambiguation of nominative and accusative cases of nouns using information on semantic relationships between words extracted from a large corpus of texts. The data on semantic relationships are presented as an ontology consisting of a set of semantic triplets or triples "subject-predicate-object". The results are implemented in experimental software for disambiguation.
Keywords: natural language processing, disambiguation, ontology, knowledge graph, semantic triple.

List of literature:
1. Zelenkov Yu.G., Segalovich I.V., Titov V.A. Probabilistic model of morphological homonymy removal based on normalizing substitutions and positions of neighboring words // Computer linguistics and intellectual technologies. Proceedings of the international seminar Dialogue. 2005. Vol. 2005. P. 188-197.
2. Sokirko A.V., Toldova S.Yu. Comparison of the efficiency of two methods for removing lexical and morphological ambiguity for the Russian language (hidden Markov model and syntactic analyzer of noun phrases) // Internet Mathematics 2005: automatic processing of web data. Moscow, 2005. P. 80-94.
3. Lakomkin E.D., Puzyrevsky I.V, Ryzhova D.A. Analysis of statistical algorithms for removing morphological homonymy in the Russian language. [Electronic resource] URL: http://aistconf.org/stuff/aist2013/submissions/aist2013_submission_33.pdf (accessed 20.12.2021).
4. Schmid H. Probabilistic part-ofispeech tagging using decision trees // New methods in language processing. 2013. P. 154-164.
5. Sharoff S., Kopotev M., Erjavec T., Feldman A., Divjak D. Designing and evaluating a Russian tagset // Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC 2008). Vol. 26. P. 279-285.
6. Muzychka S.A., Romanenko A.A., Piontkovskaja I.I. Conditional Random Field for morphological disambiguation in Russian // Компьютерная лингвистика и интеллектуальные технологии. 2014. С. 455-465.
7. Antonova A., Soloviev A. Using the method of conditional random fields for processing texts in Russian // Computer linguistics and intellectual technologies. 2013. P. 27–44.
8. Sorokin A., et al. MorphoRuEval-2017: an Evaluation Track for the Automatic Morphological Analysis Methods for Russian // Proceedings of the International Conference Dialogue 2017. 2017. V.1. Pp. 297-313.
9. Lyashevskaya O.N., et al. GRAMEVAL 2020 shared task: Russian full morphology and universal dependencies parsing // Proceedings of the International Conference Dialogue 2020. 2020. V.1. Pp. 553-569.
10. Chiche A., Yitagesu B. Part of speech tagging: a systematic review of deep learning and machine learning aPproaches// Journal of Big Data. 2022. V.9. №1. Pp. 1-25.
11. Smirnov I.V. Intelligent text analysis based on methods of multi-level natural language processing. - M.: FRC IU RAS, 2023. - 356 p.
12. Anastasyev, D. G. Part-of-speech tagging with rich language description / D. G. Anastasyev, A. I. Andrianov, E. M. Indenbom // Computational linguistics and intellectual technologies: Based on the materials of the annual International Conference "Dialogue", Moscow, May 31 - 03 2017 / Russian State University for the Humanities. Vol. Issue 16 (23), Volume 1. - Moscow: Russian State University for the Humanities, 2017. - P. 2-13.
13. National Corpus of the Russian Language [Electronic resource]. – Access mode: URL: https://ruscorpora.ru/ (date of access: 20.11.2024).
14. Kopotev, M.V. National Corpus of the Russian Language / M.V. Kopotev, L.A. Yanda // Voprosy yazykoznaniya . – 2006. – No. 5. – P. 149–155.
15. Zhevnerovich, E.E. Corpus of texts in scientific research / E.E. Zhevnerovich // Proceedings of the II International Scientific and Practical Conference “Linguistics, Lingvodidactics, Linguistics and Cultural Studies: Current Issues and Development Prospects” (Minsk, March 1–2, 2018). – 2018. – P.25–32.
16. Nitsenko, A. V. Using the data of the National Corpus of the Russian language to remove the homonymy of the accusative and genitive cases within the paradigm of nouns / A. V. Nitsenko, V. Yu. Shelepov // Artificial intelligence: theoretical aspects, practical application: materials of the Donetsk international scientific round table. - Donetsk: FGBNU "IPII", 2023. - 252 p. - P. 137-141.
17. Lyashevskaya O. N., Sharov S. A. Frequency dictionary of the modern Russian language (based on the materials of the National Corpus of the Russian language). // M .: Azbukovnik, 2009. - 1112 p.

Release: 4(35)'2024
Chapter: ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
How to quote: Nicenko A. V. Shelepov V. Ju. THE USE OF SEMANTIC INFORMATION TO DISAMBIGUATE THE NOMINATIVE/ACCUSATIVE HOMONYMS: AN ELEMENT OF CREATING ONTOLOGY [Text] / A. V. Nicenko V. Ju. Shelepov // Problems of artificial intelligence. - 2024. № 4 (34). - P. 16-24. - http://paijournal.guiaidn.ru/ru/2024/4(35)-2.html

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