O‘ZBEK TILIDA IFODALANGAN HUJJATLARNI KALITLI KOMPONENTALAR ASOSIDA UMUMLASHTIRISH

Авторы

  • Xamdam Kenjayev Muhammad al-Xorazmiy nomidagi TATU Nukus filiali
  • Begench Geldibayev Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti

Ключевые слова:

fragment, andoza, hujjatlar tahlili, fragment andozalari

Аннотация

Ushbu maqolada o‘zbek tilida ifodalangan hujjatlarni kalitli komponentalar asosida avtomatlashtirilgan tahlil qilish va umumlashtirish tizimi taqdim etiladi. Mazkur ARS-Uz (O‘zbek matnlaridan avtomatik hisobot shakllantiruvchi tizim) deb nomlangan tizim katta hajmdagi hujjatlar to‘plamidagi asosiy ma’lumotlarni qo‘lda umumlashtirish jarayonini yengillashtirishga yordam beradi. Tizim hujjat andozalari va fragmentlarini matematik modellashtirish, kalit so‘zlar bazasi yordamida hujjat tarkibini aniqlash va kerakli ma’lumotlarni avtomatik ravishda chiqarib olish imkonini beradi. Maqolada fragmentlarni ajratib olish, ma’lumotlarni qayta ishlash va hisobotlar tayyorlash uchun algoritmlar hamda tizimning infratuzilmasi haqida batafsil ma’lumot beriladi. Taklif etilgan yechim inson ishtirokini kamaytirish va hujjatlardagi ma’lumotlar yaxlitligini ta’minlashni maqsad qilgan.

Библиографические ссылки

A.Hassaine, S. Mecheter and A. Jaoua, “Text categorization using hyper rectangular keyword extraction: Application to news articles classification”, in: Proceedings of the Relational and Algebraic Methods in Computer Science - 15th International Conference, Braga, pp.312–325, 2015.

Adrien Bougouin, Florian Boudin, Béatrice Daille. TopicRank: Graph-Based Topic Ranking for Keyphrase extraction. Proceedings of the Sixth International Joint Conference on Natural Language Processing Conference. Conference October 2013

C.Sun, L.Hu, S.Li, T.Li, H.Li, and L.Chi, “A review of unsupervised keyphrase extraction methods using withincollection resources,” Symmetry, vol.12, no.11, pp.1–20, 2020.

Jiapeng Wang and Yihong Dong. Measurement of Text Similarity: A Survey // MDPI, Information 2020, 11, 421; doi:10.3390/info11090421

N.Firoozeh, A.Nazarenko, F Alizon, and B.Daille, “Keyword extraction: issues and methods,” Natural Language engineering, vol. 26, no. 3, pp. 259–291, 2020.

R.Campos, V.Mangaravite, A.Pasquali, A.Jorge, C.Nunes, and A.Jatowt, “Yake! Keyword extraction from single documents using multiple local features,” Information Sciences, vol. 509, pp. 257–289, 2020.

S.R.Yel-Beltagy and A.Rafea, “Kp-miner: a keyphrase extraction system for english and Arabic documents,” Information Systems, vol. 34, no. 1, pp. 132–144, 2009.

Stuart Rose, Dave engel, Automatic Keyword extraction from Individual Documents, Text Mining: Applications and Theory (pp.1 - 20), 2010. DOI: 10.1002/9780470689646.ch1

Z.Jingsheng, Z. Qiaoming, Z. Guodong, et al., “Review of research in automatic keyword extraction”, Journal of Software, Vol.28, No.9, pp.2431–2449, 2017

Z. A. Merrouni, B. Frikh and B. Ouhbi, “Automatic keyphrase extraction: A survey and trends”, Journal of Intelligent Information Systems, Vol.54, No.2, pp.391–424, 2020.

Shah, D. N., and H. Bhadka. 2017. A survey on various approaches used in named entity recognition for Indian languages. International Journal of Computer Application 167 (1):11–18. doi:10.5120/ijca2017913878.

L.A.Pizzato,D.Molla, C.Paris, Pseudo relevance feedback using named entities for question answering, in: Proceeding soft he 2006 Australian Language Technology Workshop, ALTW-2006,2006,pp.89–90

Опубликован

2024-08-28

Как цитировать

Kenjayev, X., & Geldibayev , B. (2024). O‘ZBEK TILIDA IFODALANGAN HUJJATLARNI KALITLI KOMPONENTALAR ASOSIDA UMUMLASHTIRISH. Цифровая трансформация и искусственный интеллект, 2(4), 79–84. извлечено от https://dtai.tsue.uz/index.php/dtai/article/view/v2i412