Abstract
Stemming is the processes of removing prefixes, suffixes, and infixes of a word to give the base form of the word. In this paper, we developed a new stemmer for Arabic language and introduced it as an R package called arStemmer1. We compared our stemmer with the well-known stemmer, Khoja stemmer which is one of the best performing stemmers. Our stemmer arStemmer1 outperformed Khoja in six out of seven experiments. We employed deep learning (skip-gram model) to build stop words lists with some manual filtration. The R package arStemmer1 is available for researches to use and test.