Meshesha, Million (PhD)Mamo, Getachew2020-06-042023-11-182020-06-042023-11-182009-01http://etd.aau.edu.et/handle/12345678/21419Most natural language processing systems use part-of-speech (POS) tagger as a separate module in their architecture. Specially, it is very significant for developing parser, machine translator, speech recognizer and search engines. Tagging is a process of labeling part-of speech tags to words of a text such that contextual information can be obtained from word labels. The main aim of this study is to develop part-of-speech tagger for Afaan Oromo language. After reviewing literature on Afaan Oromo grammars and identifying tag set and word categories, the study adopted Hidden Markov Model (HMM) approach and has implemented uni gram and bi gram models of vertebra algorithm. Uni gram model is used to understand word ambiguity in the language, while bi gram model is used to undertake contextual analysis of words. For training and testing purpose 159 sentences (with a total of 162 1 words) that are manually annotated sample corpus are used. The corpus is collected from different public Afaan Oromo newspapers and bulletins to make the sample corpus balanced. A database of lexical probabilities (LexProb) and transitional probabilities (Trans Prob) are developed from thi s annotated corpus. These two probabilities are from which the tagger learn and tag sequence of words in a sentence The performance of the prototype, Afaan Oromo tagger is tested using ten fold cross validation mechanism. The result shows that in both uni gram and bi gram models 87.58% and 91.97% accuracy is obtained, respectively. Based on experimental analysis, concluding remarks and recommendations are forwarded. Keywords: Natural Language processing, parts of speech tagging, Hidden Markov Model, N - Gram.enNatural Language processingparts of speech taggingHidden Markov ModelN -GramThesis