In this study, the successes of two different stemming algorithms in the text classifying are comparatively examined.
📌 3 Key Takeaways
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Objective & Hypothesis:Normal 0 21 false false false MicrosoftInternetExplorer4 Saving textual data and accessing them in many fields have become one of the basic problems nowadays.
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Key Findings & Data:The usage of these data effectively is directly related to the development of storage and access tools that will be used.
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Conclusion & Impact:In this study, the successes of two different stemming algorithms in the text classifying are comparatively examined.
Normal 0 21 false false false MicrosoftInternetExplorer4 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.
Balcı, M., Saraçoğlu, R., Taşdemir, Ş., Gölcük, A. (2015). Classification Performance of the Different Stemming Methods. International Journal of Applied Mathematics Electronics and Computers, Volume: 3 Issue: 3. https://doi.org/10.18100/ijamec.27805
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.
Lassila O., 1998, Web Metadata: A Matter of Semantics. IEEE Iternet Computing, pp. 30-37.