57

In  Natural  Language  Processing,  automatic  part-of-speech  recognition
is  important.  There  are  several  ways  to  automatically  detect  parts  of  speech;  this  article
discusses  the  use  of  a  hidden  Markov  model  based  on  the  Viterbi  algorithm  using  the
Python environment.

  • Web Address
  • DOI
  • Date of creation in the UzSCI system 03-01-2025
  • Read count 57
  • Date of publication 30-12-2024
  • Main LanguageIngliz
  • Pages39-49
Ўзбек

Tabiiy tillarni avtomatik qayta ishlashda so‘z turkumlarini avtomatik
aniqlash  muhim  ahamiyat  kasb  etadi.  So‘z  turkumlarini  avtomatik  aniqlashning  bir
nechta  usullari  mavjud  bo‘lib,  ushbu  maqolada  Python  muhitidan  foydalanib,  Viterbi
algoritmi asosida maxfiy Markov modelini qo‘llash haqida so‘z boradi.

Русский

При  автоматической  обработке  естественных  языков  важное
значение  имеет  автоматическое  распознавание  части  речи.  Существует
несколько  способов  автоматического  определения  части  речи,  в  этой  статье
рассматривается  использование  скрытой  модели  Маркова  на  основе  алгоритма
Витерби с использованием среды Python.

English

In  Natural  Language  Processing,  automatic  part-of-speech  recognition
is  important.  There  are  several  ways  to  automatically  detect  parts  of  speech;  this  article
discusses  the  use  of  a  hidden  Markov  model  based  on  the  Viterbi  algorithm  using  the
Python environment.

№ Author name position Name of organisation
1 Murodov S.A. o'qituvchi Qarshi xalqaro universiteti
№ Name of reference
1 Norov, A.M. (2020). Computer-oriented models of Uzbek linguistics //[Doctoral Dissertation, Karshi State University].
2 The Handbook of Computational Linguistics and Natural Language Processing Edited by Alexander Clark, Chris Fox and Shalom Lappin, 2010, Blackwell Publishing Ltd.
3 Jurafsky, Daniel & Martin, James. (2008). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition.
4 Mitkov, Ruslan (ed.), The Oxford Handbook of Computational Linguistics, 2nd edn, Oxford Handbooks (2022; online ed., Oxford Academic).
5 Mohamed Zakaria Kurdi (2017) Natural Language Processing and Computational Linguistics: Semantics, Discourse and Applications. John Wiley & Sons, Inc.
6 Second Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics: Proceedings. 25 June 2005. University of Michigan Ann Arbor, Michigan, USA.
7 Sudhir K Mishra (2018) Artificial Intelligence and Natural Language Processing. Cambridge Scholars Publishing.
8 Abjalova, Manzura & Ismailovich, Iskandarov. (2021). Methods of Tagging Part of Speech of Uzbek Language. 82-85. Conference: 2021 6th International Conference on Computer Science and Engineering (UBMK). DOI: 10.1109/ UBMK52708.2021.9558900.
9 https://www.geeksforgeeks.org/ – Viterbi algorithm for Hidden Markov Models (Last updated: 06 Jun, 2024).
10 Белоусов Ф.К., & Кузнецова И.Б. Технологии искусственного интеллекта в образовании: от теории к практике. –Новосибирск: Сибирское университетское издательство. 2018. – 146 c.
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