O’zbekiston respublikasi axborot texnologiyalari

Foydalanilgan adabiyotlar

1. 
   B.S.Gusev Google effektivnыy poisk kratkoye rukovodstvo, dialektika Moskva, Sankt-Peterburg, Kiyev, 2006.
   
2. 
   T.V.Batura. Matematicheskaya lingvistika i avtomaticheskaya obrabotka tekstov na yestestvennom yazыke. ucheb. posobiye / T. V. Batura ; Novosib. gos. un-t. – Novo- sibirsk : RIS NGU, 2016. – 166 s.
   
3. 
   T.V.Batura, M. V.Charinseva. Osnovы obrabotki tekstovoy informasii. Uchebnoye posobiye. Novosibirsk 2016 g.
   
4. 
   Lukashevich N.V. Avtomaticheskiy analiz tonalnosti tekstov po otnosheniyu k zadannomu obyektu i yego xarakteristikam // Elektronnыye biblioteki. 2015. T. 18. № 3-4. S. 88- 119.
   

Qo’shimcha adabiyotlar ro’yhati

1. 
   G. M. Chechin, Ye. V. Polojensev, S. V. Nijnikova "Poisk informasii v seti Internet", Metodicheskiye ukazaniya dlya studentov RGU Rostov-na-Donu 2001 g.
   
2. 
   V.N.Romanenko, G.V.Nikitina Setevoy informasionnыy poisk SPb «Professiya», 2005, 288 st.
   
3. 
   Bo Pang, Lillian Lee. Opinion Mining and Sentiment Analysis // Foundations and Trends in Information Retrieval. 2008. № 2. P. 1–135.
   
4. 
   Kobzareva T. Yu. V poiskax sintaksicheskoy strukturы: avtomaticheskiy analiz russkogo predlojeniya s oporoy na segmentasiyu. M.:RGGU, 2015.371s.
   

Axborot manbalari

5. http:/ / doi.acm.org /10.1145 / 564376. 564387.

6. 
   Zhang, Tong, and Frank J. Oles. 2001. Text categorization based on regularized linear classification methods. /R 4(l):5-31. URL: http: ist.psu. edu / zha gootext. html.
   
7. 
   Zobel, Justin, Alistair Moffat, Ross Wilkinson, and Ron Sacks-Davis. 1995. Efficient retrieval of partial documents. IP&M 31(3):361-377. doi. http : / / d.x.doi.org /10. 1016
   

/0306-4573 (94)00052 -5.13.

6. 
   https://softcatalog.info/ru/;
   
7. 
   https://rlab.ru;
   
8. 
   https://ziyonet.uz;
   
9. 
   https://dicom.spb.ru;
   

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