Gizli Markov Modeli Kullanılarak Özbek Dilinde Sözcüklerin Nitelikleri ile Etiketlemesi

Yıl 2025, Cilt: 18 Sayı: 1, 1 - 10, 26.06.2025

Şahlo Hamroyeva , Botir Elov Zılola Husaınova

https://doi.org/10.54525/bbmd.1498096

Öz

Markov modelleri, doğal dil işlemede en yaygın kullanılan makine öğrenmesi yöntemlerinden biridir. Markov zinciri ve gizli Markov modeli, dinamik sistemleri modellemek için kullanılan stokastik (rastlantısal) yöntemdir ve sistemin mevcut durumunu, önceki durumlara dayanarak tahmin eder. Tümcelerin oluşturulmasında doğru bir sözcük dizisi oluşturan Markov zinciri, DDİ çalışmalarında yaygın olarak kullanılmaktadır. Ayrıca bir tümcedeki Varlık Adlarını (NER : VAT) tanımlamak ve gizli Markov modeline dayalı Sözcükleri Nitelikleri ile Etiketlemek (SNE) için kullanılır. Markov modeline dayanarak, gizli etiketler derlemdeki etiketli sözcüklere göre tahmin edilir. Bu makale, Özbek dilinin etiketli derlemini temel alan bir Gizli Markov modeli kullanarak belirli sözcüklerin otomatik SNE etiketlemesine yönelik yöntemler ve algoritmaları ve bunların uygulanmasını sunmaktadır.

Anahtar Kelimeler

SNE (POS) etiketleme , Markov modeli , Gizli Markov modeli , Markov zinciri , Rastantısal Yöntemler , DDİ (NLP) , Sözcük Sınıfları , Eşsesli Çözümleme , Geçiş Olasılığı , Yayılma Olasılığı , Viterbi Algoritması.

Tagging Words with Their Attributes in Uzbek Language Using the Hidden Markov Model

Öz

Markov models are one of the most widely used machine learning methods in natural language processing. Markov chain and hidden Markov model are stochastic (random) methods used to model dynamic systems and predict the current state of the system based on previous states. Markov chain, which creates a correct sequence of words in the creation of sentences, is widely used in NLP studies. It is also used to identify NERs in a phrase and for POS tagging based on hidden Markov model. Based on the Markov model, latent labels are predicted based on the labeled words in the language corpus. This article presents methods and algorithms for automatic POS tagging of a given phrase using a hidden Markov model based on a labeled corpus of the Uzbek language

Anahtar Kelimeler

POS tagging , hidden Markov model , Markov chain , Hidden Markov Models , Stochastic methods , NLP , Word classes , Homophone analysis , Transition probability , Propagation probability , Viterbi algorithm

Kaynakça

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