Journals / Natural and Engineering Sciences / 2020 / Cilt: 5 - Sayı: 3

Challenges Encountered in Turkish Natural Language Processing Studies

Pages
204–211
Publication Date
2020-11-27
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In addition, summary info about natural language processing techniques, systems and various sources developed for Turkish are given.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Natural language processing is a branch of computer science that combines artificial intelligence with linguistics.
📊
Key Findings & Data:It aims to analyze a language element such as writing or speaking with software and convert it into information.
💡
Conclusion & Impact:In addition, summary info about natural language processing techniques, systems and various sources developed for Turkish are given.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Natural language processing · Turkish natural language processing · NLP

Abstract

Natural language processing is a branch of computer science that combines artificial intelligence with linguistics. It aims to analyze a language element such as writing or speaking with software and convert it into information. Considering that each language has its own grammatical rules and vocabulary diversity, the complexity of the studies in this field is somewhat understandable. For instance, Turkish is a very interesting language in many ways. Examples of this are agglutinative word structure, consonant/vowel harmony, a large number of productive derivational morphemes (practically infinite vocabulary), derivation and syntactic relations, a complex emphasis on vocabulary and phonological rules. In this study, the interesting features of Turkish in terms of natural language processing are mentioned. In addition, summary info about natural language processing techniques, systems and various sources developed for Turkish are given.

🔖

Cite This Article

TOHMA, K., KUTLU, Y. (2020). Challenges Encountered in Turkish Natural Language Processing Studies. Natural and Engineering Sciences, Cilt: 5 - Sayı: 3, 204–211. https://ajindex.com/articles/793166

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Adalı, E. (2012). Doğal Dil İşleme. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 5(2). Ali, C. B., Haddad, H., & Slimani, Y. (2019). Empirical evaluation of compounds indexing for turkish texts. Computer Speech & Language, 56, 95-106.
  2. Amasyalı, M. F., & Diri, B. (2005). Bir soru cevaplama sistemi: Baybilmiş. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 1(1). Aşliyan, R., Günel, K., & Yakhno, T. (2007, December). Detecting misspelled words in Turkish text using syllable n-gram frequencies. In International Conference on Pattern Recognition and Machine Intelligence (pp. 553-559).
  3. Springer, Berlin, Heidelberg. Berk, G., Erden, B., & Güngör, T. (2018). Turkish verbal multiword expressions corpus. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4).
  4. IEEE. Çakıroğlu, Ü., & Özyurt, Ö. (2006). Türkçe Metinlerdeki Yazım Yanlışlarına Yönelik Otomatik Düzeltme Modeli. Elektrik-Elektronik-Bilgisayar Mühendisliği Sempozyumu ve Fuarı (Eleco 2006),
  5. BURSA, TÜRKIYE, ss.7-9
  6. Çetin, F. S., & Eryiğit, G. (2018). Türkçe Hedef Tabanlı Duygu Analizi İçin Alt Görevlerin İncelenmesi–Hedef Terim, Hedef Kategori Ve Duygu Sınıfı Belirleme. Bilişim Teknolojileri Dergisi, 11(1), 43-56.
  7. Coşkun, N. (2013). Türkçe Tümcelerin Öğelerinin Bulunması(Doctoral dissertation, Fen Bilimleri Enstitüsü). Dönmez, İ., & Adalı, E. (2015). Türkçe Tümce Çözümlemede Vektör Yaklaşımı. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 15(3), 1-11.
  8. Ergün, K., Kubat, C., Çağıl, G., & Cesur, R. (2000). İnternet ortamındaki tüketici yorumlarından özet bilgi çıkarımı. Sakarya University Journal of Science, 17(1), 33-40.
  9. Eryiğit, G. (2006). Türkçe’nin bağlılık ayrıştırması (Doctoral dissertation). Eryiğit, G., Nivre, J., & Oflazer, K. (2008). Dependency parsing of Turkish. Computational Linguistics, 34(3), 357-389.
  10. Gündoğdu, Ö. E., & Duru, N. (2016). Türkçe Metin Özetlemede Kullanılan Yöntemler. 18. Akademik Bilişim Konferansı-AB'16. Güneş, A., & TantuĞ, A. C. (2018). Turkish named entity recognition with deep learning. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4).
  11. IEEE. Güzey, C. ve Oflazer, K., 1994. Spelling Correction in Agglutinative Languages, Bilkent University Department Of Computer Engineering and Information Systems Technical Report, BU-CEIS-94-01,
  12. Ankara,
  13. Turkey. Kılıçaslan, Y., Özlem, U. Ç. A. R., Güner, E. S., & Kemal, B. A. L. (2006).Otistik Ve Zihinsel Enge