The Importance of preprocessing in Turkish Text Classification

被引：0

作者：

Acikalin, Buse ^{[1
]}

Bayazit, Nilgun Guler ^{[1
]}

机构：

[1] Yildiz Tekn Univ, Matemat Muhendisligi Bolumu, Istanbul, Turkey

来源：

2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU) | 2016年

关键词：

Text Mining; Latent Dirichlet Allocation; Topic Models; Support Vector Machine; Naive Bayes; Random Forest;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this study, the effects of the application of stop words filtering and stemming methods on the classification of Turkish Texts. The documents in a corpus that consists of summaries of conference and journal articles classified by Naive Bayes, Support Vector Machines and Random Forests methods and their performers have been compaired. All the models that have employed preprocessing with stemming and stop words elimination have yielded between 2.26% and 4.94% improvement in performance to the models that have not employed such preprocessing.

引用

页码：2053 / 2056

页数：4

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