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Year 2020, Volume: 5 Issue: 3, 204 - 211, 27.11.2020
https://doi.org/10.28978/nesciences.833188

Abstract

References

  • 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.
  • 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). 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). IEEE.
  • Çakıroğlu, Ü., & Özyurt, Ö. (2006). Türkçe Metinlerdeki Yazım Yanlışlarına Yönelik Otomatik Düzeltme Modeli. Elektrik-Elektronik-Bilgisayar Mühendisliği Sempozyumu ve Fuarı (Eleco 2006), BURSA, TÜRKIYE, ss.7-9
  • 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.
  • 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.
  • 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.
  • 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.
  • 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). 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, Ankara, Turkey.
  • Kılıçaslan, Y., Özlem, U. Ç. A. R., Güner, E. S., & Kemal, B. A. L. (2006).Otistik Ve Zihinsel Engelli Çocuklar İçin Doğal Dil İşleme Tabanlı Bir Yardım Aracı: Bir Başlangıç Çalışması. Trakya Üniversitesi Fen Bilimleri Dergisi, 7(2), 101-108.
  • Küçük, D., & Arıcı, N. (2016). Türkçe için Wikipedia Tabanlı Varlık İsmi Tanıma Sistemi. Politeknik Dergisi, 19(3), 325-332.
  • Kutlugün, M. A., & Şirin, Y. (2018). Turkish meaningful text generation with class based n-gram model. In 2018 26th Signal Processing and Communications Applications Conference (SIU)(pp. 1-4). IEEE.
  • Oflazer, K. ve Solak, A., 1992. Parsing Agglutinative Word Structures And Its Application to Spelling Checking for Turkish, In Proceedings of the 15thInternational Conference On Computational Linguistics, Nantes, France, August 23-28, p. 39-45.
  • Oflazer, K., 1993. Two-level Description Of Turkish Morphology, In Proceedings of the Sixth Conference Of The Europen Chapter Of The Assotiation For Computational Linguistics, Utrecht, Netherlands, April 1993.
  • Oflazer, K. (2014). Turkish and its challenges for language processing. Language resources and evaluation, 48(4), 639-653.
  • Oyucu, S., Sever, H., & Polat, H. (2019). Overview of automatic speech recognition, approaches and challenges: Way the future to Turkish speech recognition. Gazi Univ. Sci. J. Part C Des. Technol, 7, 834-854.
  • Ozyurt, O., & Kose, C. Türkçe Tabanlı Diyalog Sistemi Tasarimi ve Kodlanmasi Polat, N., Mahalingappa, L., & Mancilla, R. L. (2020). Longitudinal growth trajectories of written syntactic complexity: The case of Turkish learners in an intensive English program. Applied Linguistics, 41(5), 688-711.
  • Sak, H., Güngör, T., & Saraçlar, M. (2011). Resources for Turkish morphological processing. Language resources and evaluation, 45(2), 249-261.
  • Saygılı, N. Ş., Amghar, T., Levrat, B., & Acarman, T. (2017, May). Taking advantage of Turkish characteristic features to achieve authorship attribution problems for Turkish. In 2017 25th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • Solak, A., (1991). Design And Implementation of A Spelling Checker For Turkish, M.S. Thesis, Bilkent University, Ankara.
  • Şahin, G. G., & Adalı, E. (2018). Annotation of semantic roles for the Turkish proposition bank. Language Resources and Evaluation, 52(3), 673-706.
  • Topçu, S., Şen, C., Amasyalı, M.F,.(2012). Türkçe Sohbet Robotu. Akıllı Sistemler ve Uygulamaları, ASYU.
  • Yeniterzi, R., & Oflazer, K. (2010, July). Syntax-to-morphology mapping in factored phrase-based statistical machine translation from English to Turkish. In Proceedings of the 48th annual meeting of the association for computational linguistics (pp. 454-464).
  • Yuret, D., & Türe, F. (2006, June). Learning morphological disambiguation rules for Turkish. In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference (pp. 328-334).

Challenges Encountered in Turkish Natural Language Processing Studies

Year 2020, Volume: 5 Issue: 3, 204 - 211, 27.11.2020
https://doi.org/10.28978/nesciences.833188

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.

References

  • 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.
  • 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). 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). IEEE.
  • Çakıroğlu, Ü., & Özyurt, Ö. (2006). Türkçe Metinlerdeki Yazım Yanlışlarına Yönelik Otomatik Düzeltme Modeli. Elektrik-Elektronik-Bilgisayar Mühendisliği Sempozyumu ve Fuarı (Eleco 2006), BURSA, TÜRKIYE, ss.7-9
  • 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.
  • 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.
  • 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.
  • 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.
  • 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). 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, Ankara, Turkey.
  • Kılıçaslan, Y., Özlem, U. Ç. A. R., Güner, E. S., & Kemal, B. A. L. (2006).Otistik Ve Zihinsel Engelli Çocuklar İçin Doğal Dil İşleme Tabanlı Bir Yardım Aracı: Bir Başlangıç Çalışması. Trakya Üniversitesi Fen Bilimleri Dergisi, 7(2), 101-108.
  • Küçük, D., & Arıcı, N. (2016). Türkçe için Wikipedia Tabanlı Varlık İsmi Tanıma Sistemi. Politeknik Dergisi, 19(3), 325-332.
  • Kutlugün, M. A., & Şirin, Y. (2018). Turkish meaningful text generation with class based n-gram model. In 2018 26th Signal Processing and Communications Applications Conference (SIU)(pp. 1-4). IEEE.
  • Oflazer, K. ve Solak, A., 1992. Parsing Agglutinative Word Structures And Its Application to Spelling Checking for Turkish, In Proceedings of the 15thInternational Conference On Computational Linguistics, Nantes, France, August 23-28, p. 39-45.
  • Oflazer, K., 1993. Two-level Description Of Turkish Morphology, In Proceedings of the Sixth Conference Of The Europen Chapter Of The Assotiation For Computational Linguistics, Utrecht, Netherlands, April 1993.
  • Oflazer, K. (2014). Turkish and its challenges for language processing. Language resources and evaluation, 48(4), 639-653.
  • Oyucu, S., Sever, H., & Polat, H. (2019). Overview of automatic speech recognition, approaches and challenges: Way the future to Turkish speech recognition. Gazi Univ. Sci. J. Part C Des. Technol, 7, 834-854.
  • Ozyurt, O., & Kose, C. Türkçe Tabanlı Diyalog Sistemi Tasarimi ve Kodlanmasi Polat, N., Mahalingappa, L., & Mancilla, R. L. (2020). Longitudinal growth trajectories of written syntactic complexity: The case of Turkish learners in an intensive English program. Applied Linguistics, 41(5), 688-711.
  • Sak, H., Güngör, T., & Saraçlar, M. (2011). Resources for Turkish morphological processing. Language resources and evaluation, 45(2), 249-261.
  • Saygılı, N. Ş., Amghar, T., Levrat, B., & Acarman, T. (2017, May). Taking advantage of Turkish characteristic features to achieve authorship attribution problems for Turkish. In 2017 25th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • Solak, A., (1991). Design And Implementation of A Spelling Checker For Turkish, M.S. Thesis, Bilkent University, Ankara.
  • Şahin, G. G., & Adalı, E. (2018). Annotation of semantic roles for the Turkish proposition bank. Language Resources and Evaluation, 52(3), 673-706.
  • Topçu, S., Şen, C., Amasyalı, M.F,.(2012). Türkçe Sohbet Robotu. Akıllı Sistemler ve Uygulamaları, ASYU.
  • Yeniterzi, R., & Oflazer, K. (2010, July). Syntax-to-morphology mapping in factored phrase-based statistical machine translation from English to Turkish. In Proceedings of the 48th annual meeting of the association for computational linguistics (pp. 454-464).
  • Yuret, D., & Türe, F. (2006, June). Learning morphological disambiguation rules for Turkish. In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference (pp. 328-334).
There are 30 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Kadir Tohma This is me

Yakup Kutlu This is me

Publication Date November 27, 2020
Submission Date June 6, 2020
Published in Issue Year 2020 Volume: 5 Issue: 3

Cite

APA Tohma, K., & Kutlu, Y. (2020). Challenges Encountered in Turkish Natural Language Processing Studies. Natural and Engineering Sciences, 5(3), 204-211. https://doi.org/10.28978/nesciences.833188

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