Artificial Intelligence's Pupil Natural Language Processing
Abstract
Artificial intelligence (AI) has become the most prominent investment area in innovations and initiatives that affect every aspect of our lives. With personal and institutional research and development studies, countries support studies and initiatives in this field. They are devoting significant resources to this field. As a result of this, they undoubtedly receive the return of their investment in this field individually, institutionally and nationally. Co-working areas that concern many fields use AI-based infrastructure and software. One of these fields of study is Natural Language Processing. Natural language processing makes use of AI-based applications especially in classification, clustering, information filtering and translation studies. This technology is especially used in machine learning, expert systems, natural language processing, speech systems, improvement process, vision systems and robotic systems. Although natural language processing is an application-oriented field, a descriptive study has been carried out within the general framework of this field. In this study, the main areas in which natural language processing is related to artificial intelligence will be discussed with a descriptive approach.
Keywords
Artificial Intelligence , Natural Language Processing , technology
Kaynakça
- Kaynakça
Adalı, E. "Doğal Dil İşleme”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 5 (2016 ):
- Adalı, E. (2020) Türkçe Doğal Dil İşleme, Akçağ Türkçe Doğal Dil İşleme, Akçağ Yayınları, Ankara.
- Albayrak, A. (2020) Doğal Dil İşleme Teknikleri Kullanılarak Disiplinler Arası Lisansüstü Ders İçeriği Hazırlanması. Bilişim Teknolojileri Dergisi, Cilt: 13, Sayı: 4. s. 373-383
- Bahdanau, D. Cho, K. Bengio Y. (2014) Neural machine translation by jointly learning to align and translate, Proceedings of ICLR. Ayrıca bkz.: https://arxiv.org/pdf/1409.0473v1.pdf
- Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
- Brownlee, J. (2020) 10 Clustering Algorithms With Python. https://machinelearningmastery.com/clustering-algorithms-with-python/
- Cho, K., Van Merri Enboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder–Decoder approaches. In Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation. to appear. Ayıca bkz.: https://arxiv.org/pdf/1409.1259.pdf
- Deliloğlu, R. A. S. & Pehlivanlı, A.Ç. (2021). Hibrit Açıklanabilir Yapay Zekâ Tasarımı ve LIME Uygulaması. Avrupa Bilim ve Teknoloji Dergisi, (27), 228-236.
- Deutschman, Z. (2019) Multi-Label Text Classification Assign labels to movies based on descriptions. https://towardsdatascience.com/multi-label-text-classification- 5c505fdedca8
- Doğrucan, M.F. ve Hazar, Z. (2019). "Yapay Zekâ Çalışmalarında Dilsel Arka Plan ve Felsefe" Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sayı 34, Denizli, s.159-167.
