Corpus-Based Arabic Stemming Using N-Grams

In languages with high word inflation such as Arabic, stemming improves text retrieval performance by reducing words variants. We propose a change in the corpus-based stemming approach proposed by Xu and Croft for English and Spanish languages in order to stem Arabic words. We generate the conflation classes by clustering 3-gram representations of the words found in only 10% of the data in the first stage. In the second stage, these clusters are refined using different similarity measures and thresholds. We conducted retrieval experiments using row data, Light-10 stemmer and 8 different variations of the similarity measures and thresholds and compared the results. The experiments show that 3-gram stemming using the dice distance for clustering and the EM similarity measure for refinement performs better than using no stemming; but slightly worse than Light-10 stemmer. Our method potentially could outperform Light-10 stemmer if more text is sampled in the first stage.