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

Yıl 2025, Cilt: 9 Sayı: 2, 884 - 903, 20.07.2025

Fatih Özer

https://doi.org/10.34083/akaded.1705054

Öz

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.

Anahtar Kelimeler

Klasik Türk Edebiyatı Dijital Dönüşüm Yapay Zekâ Metin Madenciliği Doğal Dil İşleme

Kaynakça

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

Yıl 2025, Cilt: 9 Sayı: 2, 884 - 903, 20.07.2025

Fatih Özer

https://doi.org/10.34083/akaded.1705054

Öz

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.

Anahtar Kelimeler

Classical Turkish Literature Digital Transformation Artificial Intelligence Text Mining Natural Language Processing

Kaynakça

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  • Bunescu, R. C., & Mooney, R. J. (2007). Extracting Relations from Text: From Word Sequences to Dependency Paths. A. Kao & S. R. Poteet (Ed.), Natural Language Processing and Text Mining içinde (ss. 29–53). Springer. https://doi.org/10.1007/978-1-84628-754-1_3
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  • Dölek, İ., & Kurt, A. (2022). Osmanlıcadan Türkçeye Uçtan Uca Aktarım. Journal of Smart Systems Research, 3(1), 1–10.
  • Ece, S. (2015). Klasik Türk Edebiyatı Araştırma Yöntemleri (I–II). Eser Basım Yayın.
  • Ergün, M. (2017). Using the Techniques of Data Mining and Text Mining in Educational Research. Elektronik Eğitim Bilimleri Dergisi, 6(12), 180–189.
  • Feng, T. (2019). Cognitive Function of Artificial Intelligence Literature. IOP Conference Series: Materials Science and Engineering, 573(1), Article 012106. https://doi.org/10.1088/1757-899X/573/1/012106
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  • Hinrichs, E., & Krauwer, S. (2014). The CLARIN Research Infrastructure: Resources and Tools for e-Humanities Scholars. Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC'14) içinde (ss. 1525–1531). European Language Resources Association (ELRA).
  • İşler, N. (2021). Eski Türk Edebiyatı Çalışmalarında Dijital Kaynaklar ve Uygulamalar. BEÜ İlahiyat Fakültesi Dergisi, 8(1), 249–272. https://doi.org/10.33460/beuifd.902116
  • Jobin, A., Ienca, M., & Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
  • Kacena, M. A., Plotkin, L. I., & Fehrenbacher, J. C. (2024). The Use of Artificial Intelligence in Writing Scientific Review Articles. Current Osteoporosis Reports, 22, 115–121. https://doi.org/10.1007/s11914-023-00852-0
  • Kalpaklı, M. (2017, 27–29 Ocak). Ottoman Text Archive Project (OTAP), Computer Sciences and Digital Humanities [Konferans sunumu]. Many Poems of Baki, Simpson Center for the Humanities, University of Washington, Seattle, ABD.
  • Khan, S. H. (2023). AI at Doorstep: ChatGPT and Academia. Journal of the College of Physicians and Surgeons Pakistan, 33(10), 1085–1086. https://doi.org/10.29271/jcpsp.2023.10.1085
  • Kılınç, A. (2021). Fuzûlî Dîvânı. Türkiye Yazma Eserler Kurumu Başkanlığı. http://ekitap.yek.gov.tr/urun/fuz%C3%BBli-divani_765.aspx
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  • MESTAP. (t.y.). Mestap. Erişim tarihi: 15 Nisan 2025, https://mestap.com/
  • Meyer, J. G., Urbanowicz, R. J., Martin, P. C. N., O'Connor, K., Li, R., Peng, P. C., Moore, J. H., & McKinney, B. A. (2023). ChatGPT and Large Language Models in Academia: Opportunities and Challenges. BioData Mining, 16, Article 20. https://doi.org/10.1186/s13040-023-00339-9
  • Miao, J., Thongprayoon, C., Suppadungsuk, S., Garcia Valencia, O. A., Qureshi, F., & Cheungpasitporn, W. (2024). Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review. Clinics and Practice, 14(1), 89–105. https://doi.org/10.3390/clinpract14010008
  • Moretti, F. (2005). Graphs, Maps, Trees: Abstract Models for a Literary History. Verso.
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  • Pollak, S., Martinc, M., & Poniž, K. M. (2020). Natural Language Processing for Literary Text Analysis: Word-Embeddings-Based Analysis of Zofka Kveder's Work. R. Vieira, M. J. Finatto, S. Luz, & S. Pollak (Ed.), Proceedings of DHandNLP2020: Digital Humanities and Natural Language Processing içinde (ss. 33–42). CEUR Workshop Proceedings. https://ceur-ws.org/Vol-2607/paper4.pdf
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Toplam 56 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Türk Dili ve Edebiyatı (Diğer)
Bölüm Makaleler
Yazarlar

Fatih Özer 0000-0003-4512-7147

Yayımlanma Tarihi 20 Temmuz 2025
Gönderilme Tarihi 23 Mayıs 2025
Kabul Tarihi 9 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 9 Sayı: 2

Kaynak Göster

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
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