The KNN based Uyghur Text Classification and its Performance Analysis

Abstract

This paper takes the automatic classification of the large-scale Uyghur text collected from the network as research background, designed the functional block structure of the Uyghur text classification system, and chose the KNN algorithm as the classification engine, and programmed the classification system using C sharp. In the preprocessing part, combining with the Uyghur language's lexical characteristics, we introduced the stem extraction method into the procedure, and then have greatly reduced the whole feature dimensions. the classification experimental results on the basis of large-scale text corpus includes more than 3000 documents which are belongs to different 10 categories are given, and the results of the classification experiments for the different number of features selected by using x2 statistical method are also given. The results show that only 3% to 5% of the whole high dimensional features are crucial to higher classification accuracy, so it is possible how to determine what those best features are or further reducing the feature space dimensions which are the interesting issues to be further continued.

A Robust Morpheme Sequence and Convolutional Neural Network-Based Uyghur and Kazakh Short Text Classification

“…Previous works [4] on stem extraction for Uyghur and Kazakh texts are mostly based on simple suffix-based stemming methods and some simple hand-crafted rules, which suffer from ambiguity, particularly on the short texts. Sentence or longer context-based reliable stem extraction methods can extract stems and terms accurately in noisy texts for Uyghur and Kazakh texts on a sentence level, and lead to the ambiguity reduction in a noisy text environment.…”

“…Some works on Uyghur and Kazakh text classification have been reported in [4,9,10]. Tuerxun et al [4] used KNN (K-Nearest Neighbor) as a classifier on Uyghur text to conduct text classification, and used the TFIDF (Term Frequency-Inverse Document Frequency) algorithm to calculate the feature weight in this paper.…”

“…Some works on Uyghur and Kazakh text classification have been reported in [4,9,10]. Tuerxun et al [4] used KNN (K-Nearest Neighbor) as a classifier on Uyghur text to conduct text classification, and used the TFIDF (Term Frequency-Inverse Document Frequency) algorithm to calculate the feature weight in this paper. Imam et al [9] used the TextRank algorithm to select the features to make a sentiment classification experiment on Uyghur text, and SVM (Support Vector Machine) was used as a classifier in this experiment.…”