An Amharic stemmer
Atelach Alemu Argaw, Lars Asker · 2007
Stemming is an important analysis step in a number of areas such as natural language processing (NLP), information retrieval (IR), machine translation(MT) and text classification.In this paper we present the development of a stemmer for Amharic that reduces words to their citation forms.Amharic is a Semitic language with rich and complex morphology.The application of such a stemmer is in dictionary based cross language IR, where there is a need in the translation step, to look up terms in a machine readable dictionary (MRD).We apply a rule based approach supplemented by occurrence statistics of words in a MRD and in a 3.1M words news corpus.The main purpose of the statistical supplements is to resolve ambiguity between alternative segmentations.The stemmer is evaluated on Amharic text from two domains, news articles and a classic fiction text.It is shown to have an accuracy of 60% for the old fashioned fiction text and 75% for the news articles.