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Klasik Türk Edebiyatı Çalışmalarında Yapay Zekâ Kullanımı Üzerine Gelecek Perspektifleri ve Öneriler

Year 2025, Volume: 9 Issue: 2, 884 - 903, 20.07.2025
https://doi.org/10.34083/akaded.1705054

Abstract

Bu çalışma, klasik Türk edebiyatı araştırmalarında yapay zekâ teknolojilerinin dijital dönüşüm bağlamındaki yerini ve potansiyelini incelemektedir. Araştırma, metin eleştirisi, edisyon kritik, dilbilimsel inceleme, kültürel ve tarihsel bağlam analizi gibi geleneksel filolojik yöntemlerin ötesine geçerek metin madenciliği, doğal dil işleme ve makine öğrenimi gibi yapay zekâ tekniklerinin edebî metin analizindeki işlevselliğini teorik ve uygulamalı boyutlarıyla tartışmaktadır. Dijital teknolojilerin yüksek veri işleme kapasitesi, büyük metin külliyatları üzerinde yapılacak korpus tabanlı analizlerle edebiyatın estetik ve kültürel boyutlarının daha kapsamlı incelenmesine olanak sağlamaktadır. Makine öğrenimi algoritmaları, özellikle büyük ölçekli metinleri analiz etme konusunda; şairlerin üsluplarını belirleme, dönemsel dil değişimlerini ortaya çıkarma ve metinler arası ilişki ağlarını haritalandırma gibi olanaklar sunarak Türk edebiyatı tarihçiliğine yeni yöntemsel yaklaşımlar kazandırmaktadır. Araştırma, söz konusu hesaplamalı tekniklerin edebî eserlerin yapısal, dilsel ve tematik boyutlarını yapılandırılmış bir şekilde analiz etme yolları üzerine metodolojik öneriler sunmayı ve bu alanda çalışma yapacak araştırmacılara dijital araçların kullanımında bir perspektif oluşturmayı amaçlamaktadır. Çalışma ayrıca, yapay zekâ uygulamalarının beraberinde getirdiği algoritmik önyargı, veri temsil sorunları ve araştırma etiği zorlukları gibi eleştirel meseleleri de irdelemektedir.

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Future Perspectives and Suggestions on the Use of Artificial Intelligence in Classical Turkish Literature Studies

Year 2025, Volume: 9 Issue: 2, 884 - 903, 20.07.2025
https://doi.org/10.34083/akaded.1705054

Abstract

This study examines the place and potential of artificial intelligence technologies in the context of digital transformation in classical Turkish literature studies. The research goes beyond traditional philological methods such as textual criticism, edition criticism, linguistic analysis, cultural and historical context analysis, and discusses the functionality of artificial intelligence techniques such as text mining, natural language processing and machine learning in literary text analysis with its theoretical and practical dimensions. The high data processing capacity of digital technologies allows for a more comprehensive examination of the aesthetic and cultural dimensions of literature through corpus-based analyses of large corpuses of texts. Machine learning algorithms offer new methodological approaches to the historiography of Turkish literature, especially in analyzing large-scale texts, such as identifying the styles of poets, revealing periodic linguistic shifts, and mapping intertextual networks. This study aims to provide methodological suggestions on the ways in which these computational techniques can analyze the structural, linguistic and thematic dimensions of literary works in a structured way and to provide a perspective on the use of digital tools for researchers in this field. The study also explores critical issues such as algorithmic bias, data representation problems, and research ethics challenges brought about by artificial intelligence applications.

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There are 56 citations in total.

Details

Primary Language Turkish
Subjects Turkish Language and Literature (Other)
Journal Section Articles
Authors

Fatih Özer 0000-0003-4512-7147

Publication Date July 20, 2025
Submission Date May 23, 2025
Acceptance Date July 9, 2025
Published in Issue Year 2025 Volume: 9 Issue: 2

Cite

APA Özer, F. (2025). Klasik Türk Edebiyatı Çalışmalarında Yapay Zekâ Kullanımı Üzerine Gelecek Perspektifleri ve Öneriler. Akademik Dil Ve Edebiyat Dergisi, 9(2), 884-903. https://doi.org/10.34083/akaded.1705054