  Part-of-speech (POS) tagging is considered as one of the basic but necessary
tools 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 the
contemporary POS taggers available for English and other European languages.
The aim of this work is to improve POS tagging performance for the Amharic
language, which was never above 91%. Usage of morphological knowledge, an
extension of the existing annotated data, feature extraction, parameter tuning
by applying grid search and the tagging algorithms have been examined and
obtained significant performance difference from the previous works. We have
used three different datasets for POS experiments.
