ABSTRACT

Suffix stripping is a pre-processing step required in a number of natural language processing applications. Stemmer is a tool used to perform this step. This paper presents and evaluates a rule-based and an unsupervised Marathi stemmer. The rule-based stemmer uses a set of manually extracted suffix stripping rules whereas the unsupervised approach learns suffixes automatically from a set of words extracted from raw Marathi text. The performance of both the stemmers has been compared on a test dataset consisting of 1500 manually stemmed word.