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Sulzhenko, R. V.; Dobrov, B. V.

Approximation of the meaning for thematic subject headings by simple interpretable representations. (English) Zbl 07896215

Lobachevskii J. Math. 45, No. 3, 1261-1274 (2024).

MSC:

62-XX Statistics
62Bxx Sufficiency and information
68Txx Artificial intelligence

Keywords:

multi-label text classification; interpretable machine learning; inconsistent markup; Boolean formula

Software:

BERT
Cite Review PDF
Full Text: DOI

References:

[1] S. T. Dumais, D. D. Lewis, and F. Sebastiani, ‘‘Report on the workshop on Operational Text Classification systems (OTC-02),’’ ACM SIGIR Forum 36, 68 (2002). doi:10.1145/792550.792563
[2] M. S. Ageev and B. V. Dobrov, ‘‘Machine learning method based on modeling the logic of the rubricator,’’ in Digital Libraries: Advanced Methods and Technologies, Electronic Collections, Proceedings of the 5th All-Russia Conference RCDL’2003 (NII Khim. SPbU, St. Petersburg, 2003), pp. 150-158. http://rcdl.ru/doc/2003/B2.pdf
[3] Apte, Ch.; Damerau, F., Automated learning of decision text categorization, ACM Trans. Inform. Syst., 12, 233-251, 1994 · doi:10.1145/183422.183423
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[7] Loukachevitch, N. V.; Dobrov, B. V., The Palgrave Handbook of Digital Russia Studies, 2020, New York: Springer, New York
[8] N. V. Lukashevich, B. V. Dobrov, A. M. Pavlov, and S. V. Shternov, ‘‘Ontological resources and information and analytical system in the subject area ‘Security’,’’ Ontolog. Proektir. 1 (8), 74-95 (2018). http://ontology-of-designing.ru/article/2018_1(27)/6_Loukachevitch.pdf
[9] N. V. Lukashevich, Thesauruses in Information Retrieval Tasks, 2010. https://istina.msu.ru/download/8944241/1q8q4M:1022DY7tc6zxbUNUs6LlYFujd2c/.
[10] F. Feng, Y. Yang, D. Cer, N. Arivazhagan, and W. Wang, ‘‘Language-agnostic BERT sentence embedding,’’ in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland (Assoc. Comput. Linguist., 2022), Vol. 1, pp. 878-891.
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