Geodesic
Development of adaptive model for recognition of text inquiries
Journal of computational and engineering mathematics, Tome 4 (2017) no. 2, pp. 61-65 Cet article a éte moissonné depuis la source Math-Net.Ru

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The paper is devoted to development of adaptive model to recognize the text inquiries using nomenclature reference book. The main calculations on resolution of conflicts which occur at recognition of text inquiries are submitted. Algorithms for solving conflicts use nomenclature reference book and parameters of association rules. Using such method, we take into consideration the previous experience of recognition and adapt the model in accordance with the new results of recognition. Adaptive model to recognize the text inquiries can be applied to different areas. For example, pharmaceutical features and drugs can be recognized in medicine in such a way.
Keywords: adaptive model, text inquiry, nomenclature reference book. 
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A. S. Ambrosova. Development of adaptive model for recognition of text inquiries. Journal of computational and engineering mathematics, Tome 4 (2017) no. 2, pp. 61-65. http://geodesic.mathdoc.fr/item/JCEM_2017_4_2_a5/

[1] Bay Vo, Bac Le, “Fast Algorithm for Mining Generalized Association Rules”, International Journal of Database Theory and Application, 2:3 (2009), 1–12 http://www.sersc.org/journals/IJDTA/vol2_no3/1.pdf

[2] F. Sebastiani, “Machine Learning in Automated Text Categorization”, ACM Computing Surveys, 34:1 (2002), 1–47 http://nmis.isti.cnr.it/sebastiani/Publications/ACMCS02.pdf | DOI

[3] T. A. Zaiko, A. A. Oleinik, S. A. Subbotin, “Assotsiativnye pravila v intellektualnom analize dannykh”, Visnik NTU "KhPI" . Seriya: Informatika ta modelyuvannya, 2013, no. 39 (1012), 82–96

[4] I. A. Chubukova, Data Mining, BINOM. Laboratoriya znanii, M., 2008

[5] E. A. Sidorova, S. V. Anokhin, I. S. Kononenko, N. V. Salomatina, “Tematicheskii analiz zaprosov polzovatelei na osnove predmetno-orientirovannogo slovarya”, Vestnik Novosibirskogo gosudarstvennogo universiteta. Seriya: Informatsionnye tekhnologii, 12:4 (2014), 83–94 | MR

[6] B. V. Dobrov, N. V. Lukashevich, “Avtomaticheskaya rubrikatsiya polnotekstovykh dokumentov po klassifikatoram slozhnoi struktury”, VIII Nats. konf. po iskusstvennomu intellektu KII-2002, v. 1, Fizmatlit, M., 2002, 178–186