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Publications in Math-Net.Ru |
Citations |
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2025 |
| 1. |
F. Sadkovskii, N. Loukachevitch, I. Grishin, “Fine-tuning large language models for hypernym discovery task: sister terms do their part”, Zap. Nauchn. Sem. POMI, 546 (2025), 146–173  |
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2024 |
| 2. |
A. S. Bubnov, N. I. Gallini, I. Yu. Grishin, I. M. Kobozeva, N. V. Lukashevich, M. B. Panich, E. N. Rayevskii, F. A. Sadkovsky, R. R. Timirgaleeva, “Ontologies as a foundation for formalization of scientific information and extraction of new knowledge”, Dokl. RAN. Math. Inf. Proc. Upr., 520:1 (2024), 82–89  ; Dokl. Math., 110:3 (2024), 521–527 |
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2023 |
| 3. |
A. Rogov, N. Lukashevich, “Automatic evaluation of interpretability methods in text categorization”, Zap. Nauchn. Sem. POMI, 530 (2023), 68–79 ; J. Math. Sci. (N. Y.), 285:2 (2024), 201–209 |
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2022 |
| 4. |
D. Yu. Turdakov, A. I. Avetisyan, K. V. Arkhipenko, A. V. Antsiferova, D. S. Vatolin, S. S. Volkov, A. V. Gasnikov, D. A. Devyatkin, M. D. Drobyshevskiy, A. P. Kovalenko, M. I. Krivonosov, N. V. Lukashevich, V. A. Malykh, S. I. Nikolenko, I. V. Oseledets, A. I. Perminov, I. V. Sochenkov, M. M. Tihomirov, A. N. Fedotov, M. Yu. Khachay, “Trusted artificial intelligence: challenges and promising solutions”, Dokl. RAN. Math. Inf. Proc. Upr., 508 (2022), 13–18 ; Dokl. Math., 106:suppl. 1 (2022), S9–S13 |
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| 5. |
I. E. Kalabikhina, N. V. Lukashevich, E. P. Banin, K. V. Alibaeva, “Automated analysis of family values of vkontakte users”, Intelligent systems. Theory and applications, 26:1 (2022), 190–195 |
| 6. |
N. V. Lukashevich, “Automatic sentiment analysis of texts: problems and methods”, Intelligent systems. Theory and applications, 26:1 (2022), 50–61 |
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2021 |
| 7. |
I. V. Denisov, I. S. Rozhkov, N. V. Lukashevich, “NEREL: A Russian dataset with nested named entities and relations”, Intelligent systems. Theory and applications, 25:4 (2021), 243–249 |
| 8. |
I. E. Kalabikhina, N. V. Lukashevich, E. P. Banin, K. V. Alibaeva, S. M. Rebrey, “Automatic extraction of social network users' attitudes on reproductive behavior issues”, Program Systems: Theory and Applications, 12:4 (2021), 33–63 |
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| 9. |
A. S. Bolshina, N. V. Lukashevich, “Weakly supervised word sense disambiguation using automatically labelled collections”, Proceedings of ISP RAS, 33:6 (2021), 193–204 |
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2017 |
| 10. |
N. L. Rusnachenko, N. V. Lukashevich, “Methods of lexicon integration with machine learning for sentiment analysis system”, Artificial Intelligence and Decision Making, 2017, no. 2, 78–89 |
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2015 |
| 11. |
N. V. Loukachevitch, I. I. Chetviorkin, “Combining corpus and thesaurus information for extracting sentiment words”, Sistemy i Sredstva Inform., 25:1 (2015), 20–33 |
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| 12. |
M. A. Nokel, N. V. Lukashevich, “Topic models: adding bigrams and taking account of the similarity between unigrams and bigrams”, Num. Meth. Prog., 16:2 (2015), 215–234 |
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2014 |
| 13. |
N. V. Lukashevich, I. I. Chetviorkin, “Open evaluating sentiment analysis systems in Russian”, Artificial Intelligence and Decision Making, 2014, no. 1, 25–33 ; Scientific and Technical Information Processing, 41:6 (2014), 370–376 |
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2013 |
| 14. |
N. V. Loukachevitch, I. I. Chetviorkin, “Construction of a Model for the Cross-Domain Opinion Word Extraction”, Model. Anal. Inform. Sist., 20:2 (2013), 70–79 |
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2011 |
| 15. |
A. A. Alekseev, N. V. Lukashevich, “News cluster structure as a basis of automatic entity detection”, Artificial Intelligence and Decision Making, 2011, no. 4, 51–59 |
| 16. |
N. V. Lukashevich, I. I. Chetviorkin, “Extraction and use of opinion words for the three-way review
classification problem”, Num. Meth. Prog., 12:4 (2011), 73–81 |
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2010 |
| 17. |
N. V. Lukashevich, Yu. M. Logachev, “Automatic term extraction based on feature combination”, Num. Meth. Prog., 11:4 (2010), 108–116 |
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2008 |
| 18. |
M. S. Ageev, B. V. Dobrov, N. V. Loukachevitch, “Automatic Text Categorization: Methods and Problems”, Kazan. Gos. Univ. Uchen. Zap. Ser. Fiz.-Mat. Nauki, 150:4 (2008), 25–40 |
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2007 |
| 19. |
B. V. Dobroff, N. V. Loukachevitch, “Linguistic ontology on natural sciences and technologies for information-retrieval applications”, Kazan. Gos. Univ. Uchen. Zap. Ser. Fiz.-Mat. Nauki, 149:2 (2007), 49–72 |
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