BibTex RIS Cite

Classification Performance of the Different Stemming Methods

Year 2015, Volume: 3 Issue: 3, 208 - 210, 29.06.2015

Mehmet Balcı , Rıdvan Saraçoğlu Şakir Taşdemir Adem Gölcük

https://doi.org/10.18100/ijamec.27805

Abstract

Saving textual data and accessing them in many fields have become one of the basic problems nowadays. The usage of these data effectively is directly related to the development of storage and access tools that will be used. Therefore, software programs using different methods have been developed. One of the points that need to be taken into account is data classifying. Because using raw data in these classifying processes is harmful, finding the stem of the texts is useful. In this study, the successes of two different stemming algorithms in the text classifying are comparatively examined.

Keywords

Text Processing; Classification; kNN; Stemming.

References

  • Kantardzic M., 2003, Data Mining:Concepts, Models, Methods, and Algorithms, IEEE Pres, Wiley Interscience Publications.
  • Saracoğlu R., 2007, Searching For Similar Documents Using Fuzzy Clustering, PhD Thesis, Graduate School of Natural and Applied Sciences, Selçuk University, Konya.
  • Yıldırım P.(*), Uludağ M.(**), Görür A.(*), 2008, Hastane Bilgi Sistemlerinde Veri Madenciliği, Akademik Bilişim Konferansları’08, (*) Çankaya Üniversitesi, Bilgisayar Mühendisliği Bölümü, Ankara. (**) European Bioinformatics Institute, Cambridge, UK.
  • Porter, M.F., 1980, An Algorithm For Suffix Stripping, Program, 14(3):130-137.
  • Jurafsky, D. and Martin, J., 2000, Speech and Language Processing, Prentice Hall, New Jersey.
  • Kesgin F., 2007, Topıc Detectıon System For Turkısh Texts, Master Thesis, Graduate School of Natural and Applied Sciences, Istanbul Technical University,Istanbul.
  • Joachims, T., 2002, Learning to classify text using support vector machines, Kluwer Academic Publishers, Boston.
  • Jackson, P., Moulinier, I., 2002, Natural language processing for online applications: text retrieval, extraction, and categorization, Amsterdam.
  • Lassila O., 1998, Web Metadata : A Matter of Semantics. IEEE Iternet Computing, pp. 30-37.
  • Eroğlu M., 2000, A Study On The Effects Of Stemming And Thesaurus For Retrieving Information In Turkish Documents, Master Thesis, Hacettepe University, Computer Enginering, Ankara, Türkiye.
  • Keller J.M., Gray M.R., Givens J.A., A Fuzzy K-Nearest Neighbor Algorithm, Systems, Man and Cybernetics, IEEE Transactions on
  • Volume:SMC-15, Issue:41Page(s):580-585,1985.
  • Pilavcılar İ., 2007, Metin Madenciliği ile Metin Sınıflandırma(KNN Algoritması) – 3, Yazılım Mühendisliği İleri Seviye Makaleleri http://www.csharpnedir.com.
  • Balcı M., 2010 Comparative Analysis of The Longest Match Algorithm in Computer Based Text Processing, Master Thesis, Graduate School of Natural and Applied Sciences, Selçuk University, Konya.

Original Research Paper

Year 2015, Volume: 3 Issue: 3, 208 - 210, 29.06.2015

Mehmet Balcı , Rıdvan Saraçoğlu Şakir Taşdemir Adem Gölcük

https://doi.org/10.18100/ijamec.27805

Abstract

References

  • Kantardzic M., 2003, Data Mining:Concepts, Models, Methods, and Algorithms, IEEE Pres, Wiley Interscience Publications.
  • Saracoğlu R., 2007, Searching For Similar Documents Using Fuzzy Clustering, PhD Thesis, Graduate School of Natural and Applied Sciences, Selçuk University, Konya.
  • Yıldırım P.(*), Uludağ M.(**), Görür A.(*), 2008, Hastane Bilgi Sistemlerinde Veri Madenciliği, Akademik Bilişim Konferansları’08, (*) Çankaya Üniversitesi, Bilgisayar Mühendisliği Bölümü, Ankara. (**) European Bioinformatics Institute, Cambridge, UK.
  • Porter, M.F., 1980, An Algorithm For Suffix Stripping, Program, 14(3):130-137.
  • Jurafsky, D. and Martin, J., 2000, Speech and Language Processing, Prentice Hall, New Jersey.
  • Kesgin F., 2007, Topıc Detectıon System For Turkısh Texts, Master Thesis, Graduate School of Natural and Applied Sciences, Istanbul Technical University,Istanbul.
  • Joachims, T., 2002, Learning to classify text using support vector machines, Kluwer Academic Publishers, Boston.
  • Jackson, P., Moulinier, I., 2002, Natural language processing for online applications: text retrieval, extraction, and categorization, Amsterdam.
  • Lassila O., 1998, Web Metadata : A Matter of Semantics. IEEE Iternet Computing, pp. 30-37.
  • Eroğlu M., 2000, A Study On The Effects Of Stemming And Thesaurus For Retrieving Information In Turkish Documents, Master Thesis, Hacettepe University, Computer Enginering, Ankara, Türkiye.
  • Keller J.M., Gray M.R., Givens J.A., A Fuzzy K-Nearest Neighbor Algorithm, Systems, Man and Cybernetics, IEEE Transactions on
  • Volume:SMC-15, Issue:41Page(s):580-585,1985.
  • Pilavcılar İ., 2007, Metin Madenciliği ile Metin Sınıflandırma(KNN Algoritması) – 3, Yazılım Mühendisliği İleri Seviye Makaleleri http://www.csharpnedir.com.
  • Balcı M., 2010 Comparative Analysis of The Longest Match Algorithm in Computer Based Text Processing, Master Thesis, Graduate School of Natural and Applied Sciences, Selçuk University, Konya.
There are 14 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

Mehmet Balcı

Rıdvan Saraçoğlu This is me

Şakir Taşdemir This is me

Adem Gölcük This is me

Publication Date June 29, 2015
Published in Issue Year 2015 Volume: 3 Issue: 3

Cite

APA Balcı, M., Saraçoğlu, R., Taşdemir, Ş., Gölcük, A. (2015). Classification Performance of the Different Stemming Methods. International Journal of Applied Mathematics Electronics and Computers, 3(3), 208-210. https://doi.org/10.18100/ijamec.27805
AMA Balcı M, Saraçoğlu R, Taşdemir Ş, Gölcük A. Classification Performance of the Different Stemming Methods. International Journal of Applied Mathematics Electronics and Computers. June 2015;3(3):208-210. doi:10.18100/ijamec.27805
Chicago Balcı, Mehmet, Rıdvan Saraçoğlu, Şakir Taşdemir, and Adem Gölcük. “Classification Performance of the Different Stemming Methods”. International Journal of Applied Mathematics Electronics and Computers 3, no. 3 (June 2015): 208-10. https://doi.org/10.18100/ijamec.27805.
EndNote Balcı M, Saraçoğlu R, Taşdemir Ş, Gölcük A (June 1, 2015) Classification Performance of the Different Stemming Methods. International Journal of Applied Mathematics Electronics and Computers 3 3 208–210.
IEEE M. Balcı, R. Saraçoğlu, Ş. Taşdemir, and A. Gölcük, “Classification Performance of the Different Stemming Methods”, International Journal of Applied Mathematics Electronics and Computers, vol. 3, no. 3, pp. 208–210, 2015, doi: 10.18100/ijamec.27805.
ISNAD Balcı, Mehmet et al. “Classification Performance of the Different Stemming Methods”. International Journal of Applied Mathematics Electronics and Computers 3/3 (June 2015), 208-210. https://doi.org/10.18100/ijamec.27805.
JAMA Balcı M, Saraçoğlu R, Taşdemir Ş, Gölcük A. Classification Performance of the Different Stemming Methods. International Journal of Applied Mathematics Electronics and Computers. 2015;3:208–210.
MLA Balcı, Mehmet et al. “Classification Performance of the Different Stemming Methods”. International Journal of Applied Mathematics Electronics and Computers, vol. 3, no. 3, 2015, pp. 208-10, doi:10.18100/ijamec.27805.
Vancouver Balcı M, Saraçoğlu R, Taşdemir Ş, Gölcük A. Classification Performance of the Different Stemming Methods. International Journal of Applied Mathematics Electronics and Computers. 2015;3(3):208-10.
 Download Cover Image

Creative Commons License