Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
On arabic search: improving the retrieval effectiveness via a light stemming approach
Proceedings of the eleventh international conference on Information and knowledge management
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Support Vector Machines for Text Categorization
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 4 - Volume 4
Automatic Text Categorization: Case Study
SBRN '02 Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN'02)
OCFS: optimal orthogonal centroid feature selection for text categorization
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A text categorization based on summarization technique
RANLPIR '00 Proceedings of the ACL-2000 workshop on Recent advances in natural language processing and information retrieval: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 11
Machine learning for Arabic text categorization: Research Articles
Journal of the American Society for Information Science and Technology
A novel feature selection algorithm for text categorization
Expert Systems with Applications: An International Journal
Automatic Arabic document categorization based on the Naïve Bayes algorithm
Semitic '04 Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages
The impact of preprocessing on text classification
Information Processing and Management: an International Journal
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This paper presents and compares three feature reduction techniques that were applied to Arabic text. The techniques include stemming, light stemming, and word clusters. The effects of the aforementioned techniques were studied and analyzed on the K-nearest-neighbor classifier. Stemming reduces words to their stems. Light stemming, by comparison, removes common affixes from words without reducing them to their stems. Word clusters group synonymous words into clusters and each cluster is represented by a single word. The purpose of employing the previous methods is to reduce the size of document vectors without affecting the accuracy of the classifiers. The comparison metric includes size of document vectors, classification time, and accuracy (in terms of precision and recall). Several experiments were carried out using four different representations of the same corpus: the first version uses stem-vectors, the second uses light stem-vectors, the third uses word clusters, and the fourth uses the original words (without any transformation) as representatives of documents. The corpus consists of 15,000 documents that fall into three categories: sports, economics, and politics. In terms of vector sizes and classification time, the stemmed vectors consumed the smallest size and the least time necessary to classify a testing dataset that consists of 6,000 documents. The light stemmed vectors superseded the other three representations in terms of classification accuracy. © 2009 Wiley Periodicals, Inc.