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Part-of-speech (POS) tagging is considered as one of the basic but necessarytools which are required for many Natural Language Processing (NLP)applications such as word sense disambiguation, information retrieval,information processing, parsing, question answering, and machine translation.Performance of the current POS taggers in Amharic is not as good as that of thecontemporary POS taggers available for English and other European languages.The aim of this work is to improve POS tagging performance for the Amhariclanguage, which was never above 91%. Usage of morphological knowledge, anextension of the existing annotated data, feature extraction, parameter tuningby applying grid search and the tagging algorithms have been examined andobtained significant performance difference from the previous works. We haveused three different datasets for POS experiments.

Machine Learning Approaches for Amharic Parts-of-speech Tagging