TEMATIK MODELLASHTIRISHNING ZAMONAVIY USULLARI

Botir Elov; Alayev Ruhillo; Narzillo Aloyev

Авторы

Botir Elov Alisher Navoiy nomidagi Toshkent davlat o‘zbek tili va adabiyoti universiteti https://orcid.org/0000-0001-5032-6648
Alayev Ruhillo National University of Uzbekistan after Mirzo Ulugbek https://orcid.org/0000-0003-3757-7711
Narzillo Aloyev Tashkent State of University of Uzbek language and literature https://orcid.org/0009-0009-4625-5539

Ключевые слова:

Tematik modellashtirish, Yashirin Dirixle tadqsimoti (LDA), LSA, PLSA, lda2vec, tBERT, til korpusi, nazoratsiz mashinali o'qitish usullari

Аннотация

Tematik modellashtirish – tabiiy tilni qayta ishlash (Natural Language Processing, NLP) vazifalarining muhim komponenti bo`lib, turli sohalarda ishlatilishi mumkin. Tematik modellarida ikkita asosiy muammo mavjud: natijani bashorat qilish uchun ishlatilishi mumkin bo'lgan mavzularni aniqlash va allaqachon topilgan mavzularni tushunishni osonlashtirish. Ushbu maqolada, tematik modellashtirishning zamonaviy usullari hisoblangan manfiy bo'lmagan matritsa faktorizatsiyasi (Non-negative Matrix Factorization), yashirin semantik taqsimot (Latent Semantic Allocation, LSA), ehtimoliy yashirin semantik tahlil (Probabilistic Latent Semantic Analysis,PLSA), lda2vec chuqur o'rganish modeli va tBERT haqida fikr-mulohaza yuritiladi. Tematik modellashtirish odatda katta hajmdagi til korpusiga qo‘llaniladi. Tematik modellashtirish uchta turdagi so‘zlarning klasterlarini hosil qiladi – birgalikda keladigan so‘zlar; so‘zlarning taqsimlanishi va mavzu bo‘yicha so‘zlarning gistogrammasi. Ushbu maqolada o`zbek tili korpusidagi namunaviy matnlarga LDA usulini qo`llash orqali olingan natijalar keltiriladi.

Биографии авторов

Botir Elov, Alisher Navoiy nomidagi Toshkent davlat o‘zbek tili va adabiyoti universiteti

texnika fanlari falsafa doktori, dotsent

Alayev Ruhillo, National University of Uzbekistan after Mirzo Ulugbek

texnika fanlari falsafa doktori

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