Unsupervised Learning of Arabic Stemming using a Parallel Corpus 
 This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer . The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources . No parallel text is needed after the training phase . Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre . Examples and results will be given for Arabic , but the approach is applicable to any language that needs affix removal . Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules , affix lists , and human annotated text , in addition to an unsupervised component . Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text , and 96% of the performance of the proprietary stemmer above. 