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Stemming is one of the most significant preprocessing. stages in text categorization that most of the academic investigators aim to improve and optimize the accuracy of the classification task. High dimensionality of feature space is one of the challenges in text classification that can be decreased by many techniques. In stemming, high dimensionality of feature space is decreased by grouping those words that they have same grammatical forms and then getting their root. This work is dedicated to build an approach for Kurdish language classification using Reber Stemmer. Thus, an innovative approach is investigated to get the stem of words in Kurdish language by removing longest suffix and prefixes of words. This approach has a strong capability and meets the requirements in responding to the process of deleting as many of the required affixes as possible to get the stem of words in Kurdish language. The advantage of this stemmer is that it ignores the ordering list of affixes that receives correct stem for more than one words that have the same format. The stemming technique is implemented on KDC-4007 dataset that consists of eight classes. Support Vector Machine (SVM) and Decision Tree (DT or C 4.5) are used for the classification. This stemmer has been successfully compared with the Longest-Match stemmer technique. According to results, the F-measure of Reber stemmer and Longest-Match method in SVM is higher than DT. Reber stemmer in SVM for classes (religion, sport, health and education) obtained higher F-measure, while the rest of classes are lower in Longest-Match. Reber stemmer in DT for classes (religion, sport and art) had higher F-measure for Reber stemmer while in Longest match the rest of classes showed lower F-measure.

The rapid increase in the quantity of Kurdish documents over the last several years has created a need for improving information accuracy and precision in text classification and retrieval. Language stemming is an imperative pre-processing step for increasing the possibility of matching terms in a document in text classification tasks. Stemming helps reduce the total number of searchable terms within a document or query. This article proposes an active approach for stemming Kurdish Sorani texts to reduce variations of words to single terms or stems. The outcomes of the process, described in this article, demonstrate that decreasing the dimensionality of feature vectors in documents will increase the effectiveness of retrieval when the stemming process is used. This process applied for Kurdish Sorani can be adapted and applied in Kurdish Kurmanji as well for greater efficiency and effectiveness in digital text classification and applications.

A B S T R A C TStemming is one of the main important preprocessing techniques that can be used to enhance the accuracy of text classification. The key purpose of using the stemming is combining the number of words that have the same stem to decrease high dimensionality of feature space. Reducing feature space causes to decline time to construct a model and minimize the memory space. In this paper, a new stemming approach is explored for enhancing Kurdish text classification performance. Tree data structure and Porter's stemmer algorithms are incorporated for building the proposed approach. The system is assessed through using support vector machine and decision tree (C4.5) to illustrate the performance of the suggested stemmer after and before applying it. Furthermore, the usefulness of using stop words is considered before and after implementing the suggested approach.

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