期刊名称：Advanced Computing : an International Journal
印刷版ISSN：2229-726X
电子版ISSN：2229-6727
出版年度：2012
卷号：3
期号：6
DOI：10.5121/acij.2012.3607
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Representation of semantic information contained in the words is needed for any Arabic Text Mining applications. More precisely, the purpose is to better take into account the semantic dependencies between words expressed by the co-occurrence frequencies of these words. There have been many proposals to compute similarities between words based on their distributions in contexts. In this paper, we compare and contrast the effect of two preprocessing techniques applied to Arabic corpus: Root- based (Stemming), and Stem-based (Light Stemming) approaches for measuring the similarity between Arabic words with the well known abstractive model -Latent Semantic Analysis (LSA)- with a wide variety of distance functions and similarity measures, such as the Euclidean Distance, Cosine Similarity, Jaccard Coefficient, and the Pearson Correlation Coefficient. The obtained results show that, on the one hand, the variety of the corpus produces more accurate results; on the other hand, the Stem-based approach outperformed the Root-based one because this latter affects the words meanings.
