Improving K-Nearest Neighbor Efficacy for FarsiText Classification

被引：0
|
作者：
Elahimanesh, Mohammad Hossein [1 ,2 ]
BehrouzMinaei-Bidgoli [2 ,3 ]
Malekinezhad, Hossein [2 ,4 ]
机构：
[1] Islamic Azad Univ, Qazvin Branch, Qazvin, Iran
[2] Comp Res Ctr Islamic Sci, Qom, Iran
[3] Iran Univ Sci & Technol, Tehran, Iran
[4] Islamic Azad Univ, Naragh Branch, Naragh, Iran
来源：
LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | 2012年
关键词：
Text classification; N-grams of characters; K-nearest neighbor;
D O I：
暂无
中图分类号：
H0 [语言学];
学科分类号：
030303 ; 0501 ; 050102 ;
摘要：
One of the common processes in the field of text mining is text classification. Because of the complex nature of Farsi language, words with separate parts and combined verbs, the most of text classification systems are not applicable to Farsi texts. K-Nearest Neighbors (KNN) is one of the most popular used methods for text classification and presents good performance in experiments on different datasets. A method to improve the classification performance of KNN is proposed in this paper. Effects of removing or maintaining stop words, applying N-Grams with different lengths are also studied. For this study, a portion of a standard Farsi corpus called Hamshahril and articles of some archived newspapers are used. As the results indicate, classification efficiency improves by applying this approach especially when eight-grams indexing method and removing stop words are applied. Using N-grams with lengths more than 3 characters, presented very encouraging results for Farsi text classification. The Results of classification using our method are compared with the results obtained by mentioned related works.
引用
收藏
页码：1618 / 1621
页数：4
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