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Döküman dili tanıma için içerik bağımsız yeni bir yaklaşım: Açı Örüntüler

Year 2022, Volume: 37 Issue: 3, 1277 - 1292, 28.02.2022
https://doi.org/10.17341/gazimmfd.844700

Abstract

Metin madenciliğinde dil tanıma (DT), bir belgenin veya bir kısmının yazıldığı doğal dili algılama çalışmasıdır. Bu çalışmada, karakterlerin UTF-8 değerleri arasında kalan açı bilgisini kullanan metinden yeni bir dil tanıma yaklaşımı önerilmiştir. Önerilen açı yöntemi metinlerden öznitelik çıkarımı için kullanılmıştır. Açı örüntüler yöntemi istatistiksel bir yaklaşımdır. Önerilen yaklaşımı test etmek amacıyla çeşitli şekillerde oluşturulan dört veri setinin kullanılması kararlaştırılmıştır. Elde edilen öznitelikler Rastsal Orman (RO, RF, Random Forest), Destek Vektör Makinesi (DVM, SVM, Support Vector Machine), Liner Diskriminant Analiz (LDA, Linear Discriminant Analysis), Naive Bayes (NB) ve k-en yakın komşu (Knn, k-nearest neighbors) olmak üzere farklı sınıflandırma yöntemleri kullanılmıştır. Dört farklı veri seti kümesinden belirlenen DT başarım sonuçları sırası ile %96,81, %99,39, %93,31 ve %98,60 olarak gözlenmiştir. Yapılan çalışma sonucunda ulaşılan başarım sonuçlarına göre önerilen açı örüntüler yönteminin DT uygulamasında önemli ayırt edici bilgiler verdiği belirlenmiştir.

Thanks

Bu çalışma Siirt Üniversitesi Mühendislik Fakültesi MaVi Laboratuvarında yapılmıştır. Bu makalenin yazarları, verilen destekten dolayı MaVi Laboratuvar çalışanlarına teşekkür ederler.

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A new content-free approach to identification of document language: Angle Patterns

Year 2022, Volume: 37 Issue: 3, 1277 - 1292, 28.02.2022
https://doi.org/10.17341/gazimmfd.844700

Abstract

Language identification (LI) in text mining is the study of natural language perception in which a document or a part of it is written. In this study, a new language identification approach from text using the angle information between the UTF-8 values of the characters is proposed. The proposed angle method is used for feature extraction from texts. Angle patterns method is a statistical approach. It was decided to use four data sets created in various ways to test the proposed approach. The obtained features are used with different classification methods such as RF( Random Forest), SVM (Support Vector Machine), LDA (Linear Discriminant Analysis), NB (Naive Bayes) and Knn (k-nearest neighbor). LI performance results determined from four different data set sets were observed as 96.81%, 99.39%, 93.31% and 98.60%, respectively. According to the success results obtained as a result of the study, it was determined that the proposed angle patterns method gave important distinctive information in LI application.

References

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  • 4. Aksu, M. Ç., & Karaman, E. (2020). FastText ve Kelime Çantası Kelime Temsil Yöntemlerinin Turistik Mekanlar İçin Yapılan Türkçe İncelemeler Kullanılarak Karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi, (20), 311-320.
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  • 14. Sarma N., Singh S. R., Goswami, D., Influence of social conversational features on language identification in highly multilingual online conversations. Information Processing & Management, 56(1), 151-166, 2019.
  • 15. Takçı H., Ekinci E., Minimal feature set in language identification and finding suitable classification method with it, Procedia Technology, 1, 444–448, 2012.
  • 16. Gamallo P., Pichel, J. R., Alegria, I., From language identification to language distance. Physica A: Statistical Mechanics and its Applications, 484, 152-162, 2017.
  • 17. Takcı H., Soğukpınar İ., Letter based text scoring method for language identification, International Conference on Advances in Information Systems, İzmir-Türkiye, 283-290, October 20-22, 2004.
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  • 25. Öztürk, Ö., Abidin, D., & Özacar, T. (2018). Using classification algorithms for Turkish music makam recognition. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 6(3), 377-393.
  • 26. Aksu, M. Ç., & Karaman, E. (2020). FastText ve Kelime Çantası Kelime Temsil Yöntemlerinin Turistik Mekanlar İçin Yapılan Türkçe İncelemeler Kullanılarak Karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi, (20), 311-320.
  • 27. Kutlu, Y. (2020). Challenges Encountered in Turkish Natural Language Processing Studies. Natural and Engineering Sciences.
  • 28. Kuncan, M., Vardar, E., Kaplan, K., & Ertunç, H. M. (2020). Turkish handwriting recognition system using multi-layer perceptron. Journal of Mechatronics and Artificial Intelligence in Engineering, 1(2).
  • 29. Özcan, T , Baştürk, A . (2020). ERUSLR: Yeni bir Türkçe işaret dili veri seti ve hiperparametre optimizasyonu destekli evrişimli sinir ağı ile tanınması . Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi , 36 (1) , 527-542 . DOI: 10.17341/gazimmfd.746793.
  • 30. Kuncan, F., Kaya, Y., & Kuncan, M. (2019). New approaches based on local binary patterns for gender identification from sensor signals. Journal of the Faculty of Engineering and Architecture of Gazi University, 34(4), 2173-2185.
  • 31. Li, G., Li, J., Ju, Z., Sun, Y., & Kong, J. (2019). A novel feature extraction method for machine learning based on surface electromyography from healthy brain. Neural Computing and Applications, 31(12), 9013-9022.
  • 32. Kuncan, M., Kaplan, K., Minaz, M. R., Kaya, Y., & Ertunc, H. M. (2020). A novel feature extraction method for bearing fault classification with one dimensional ternary patterns. ISA transactions, 100, 346-357.
  • 33. Gumaei, A., Hassan, M. M., Hassan, M. R., Alelaiwi, A., & Fortino, G. (2019). A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access, 7, 36266-36273.
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There are 68 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Tuba Noyan This is me 0000-0002-3359-2570

Fatma Kuncan 0000-0003-0712-6426

Ramazan Tekin 0000-0003-4325-6922

Yılmaz Kaya 0000-0001-5167-1101

Publication Date February 28, 2022
Submission Date December 21, 2020
Acceptance Date September 25, 2021
Published in Issue Year 2022 Volume: 37 Issue: 3

Cite

APA Noyan, T., Kuncan, F., Tekin, R., Kaya, Y. (2022). Döküman dili tanıma için içerik bağımsız yeni bir yaklaşım: Açı Örüntüler. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 37(3), 1277-1292. https://doi.org/10.17341/gazimmfd.844700
AMA Noyan T, Kuncan F, Tekin R, Kaya Y. Döküman dili tanıma için içerik bağımsız yeni bir yaklaşım: Açı Örüntüler. GUMMFD. February 2022;37(3):1277-1292. doi:10.17341/gazimmfd.844700
Chicago Noyan, Tuba, Fatma Kuncan, Ramazan Tekin, and Yılmaz Kaya. “Döküman Dili tanıma için içerik bağımsız Yeni Bir yaklaşım: Açı Örüntüler”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37, no. 3 (February 2022): 1277-92. https://doi.org/10.17341/gazimmfd.844700.
EndNote Noyan T, Kuncan F, Tekin R, Kaya Y (February 1, 2022) Döküman dili tanıma için içerik bağımsız yeni bir yaklaşım: Açı Örüntüler. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37 3 1277–1292.
IEEE T. Noyan, F. Kuncan, R. Tekin, and Y. Kaya, “Döküman dili tanıma için içerik bağımsız yeni bir yaklaşım: Açı Örüntüler”, GUMMFD, vol. 37, no. 3, pp. 1277–1292, 2022, doi: 10.17341/gazimmfd.844700.
ISNAD Noyan, Tuba et al. “Döküman Dili tanıma için içerik bağımsız Yeni Bir yaklaşım: Açı Örüntüler”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37/3 (February 2022), 1277-1292. https://doi.org/10.17341/gazimmfd.844700.
JAMA Noyan T, Kuncan F, Tekin R, Kaya Y. Döküman dili tanıma için içerik bağımsız yeni bir yaklaşım: Açı Örüntüler. GUMMFD. 2022;37:1277–1292.
MLA Noyan, Tuba et al. “Döküman Dili tanıma için içerik bağımsız Yeni Bir yaklaşım: Açı Örüntüler”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 37, no. 3, 2022, pp. 1277-92, doi:10.17341/gazimmfd.844700.
Vancouver Noyan T, Kuncan F, Tekin R, Kaya Y. Döküman dili tanıma için içerik bağımsız yeni bir yaklaşım: Açı Örüntüler. GUMMFD. 2022;37(3):1277-92.