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And the POS tagger model is compared with the previous experiments in related works using HMM. As experiments showed, HMM based POS tagging has achieved 92.77 % accuracy for Shekki’noono. With the experiments carried out, the part of speech tagger is trained on the training sets using Hidden Markov model. The collected sentences are labeled by language experts with their appropriate parts of speech for each word. For the study, we have used 1500 sentences collected from different sources such as newspapers (which includes social, economic, and political aspects), modules, textbooks, Radio Programs, and bulletins. With the purpose for developing the Shekki’noono POS tagger, we have used the stochastic Hidden Markov Model. Although, one language POS tagger cannot be directly applied for other languages POS tagger. Because those applications enhance machine to machine, machine to human, and human to human communications. So, it is compulsory to develop a part of speech tagger for languages then it is possible to work with an advanced natural language application. POS tagger is incorporated in most natural language processing tools like machine translation, information extraction as a basic component. On the other hand, many other languages do not have POS taggers like Shekki’noono language. Some of the researchers developed parts of speech taggers for different languages such as English Amharic, Afan Oromo, Tigrigna, etc. This study aims at presenting a Part of speech tagger that can assign word class to words in a given paragraph sentence. It enables people to talk with the computer in their formal language rather than machine language. Natural language processing plays a great role in providing an interface for human-computer communication. Parts of speech tagger, HMM, NLP, Shekki’noono language, Bigram Abstract

Part of speech tagger online