Building a Lemmatizer and a Spell-checker for Sorani Kurdish

The present paper aims at presenting a lemmatization and a word-level error correction system for Sorani Kurdish. We propose a hybrid approach based on the morphological rules and a n-gram language model. We have called our lemmatization and error correction systems Peyv and Renus respectively, which are the first tools presented for Sorani Kurdish to the best of our knowledge. The Peyv lemmatizer has shown 86.7% accuracy. As for Renus, using a lexicon, we have obtained 96.4% accuracy while without a lexicon, the correction system has 87% accuracy. As two fundamental text processing tools, these tools can pave the way for further researches on more natural language processing applications for Sorani Kurdish.