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Stemming is an integral part of Natural Language Processing (NLP). It's apreprocessing step in almost every NLP application. Arguably, the mostimportant usage of stemming is in Information Retrieval (IR). While there arelots of work done on stemming in languages like English, Nepali stemming hasonly a few works. This study focuses on creating a Rule Based stemmer forNepali text. Specifically, it is an affix stripping system that identifies twodifferent class of suffixes in Nepali grammar and strips them separately. Onlya single negativity prefix (Na) is identified and stripped. This study focuseson a number of techniques like exception word identification, morphologicalnormalization and word transformation to increase stemming performance. Thestemmer is tested intrinsically using Paice's method and extrinsically on abasic tf-idf based IR system and an elementary news topic classifier usingMultinomial Naive Bayes Classifier. The difference in performance of thesesystems with and without using the stemmer is analysed.

A Nepali Rule Based Stemmer and its performance on different NLP applications