Morphology has a special place in any language, including written and spoken applications. Markov method is used to labeingl and determine the role of words.emergence in software sciences has eliminated 0 and 1 computations, putting them within an infinite space of between 0,1. This characteristic of fuzzy logic has resolved ambiguity in numerous previous problems. The sentence roles in Persian language were specified based on the fuzzy logic’s capability to resolve ambiguity. In two defuzzification methods Mean Of Max, Central Average, the role of words in the sentence is identified and the success rate of each method is obtained. Finally, Mean Of Max with a success rate of 64% proved to be a defuzzifier delivering the best output among two different defuzzification methods. ** * * * * * * * * * * * * * * * * * * ****** * * * * * * * * * Manuscript profile

This paper, based on the Viterbi algorithm, selects the most likely combination of different wording from a variety of scenarios. In this regard, the Bi-gram and Unigram tags of each word, based on the letters forming the words, as well as the bigram and unigram labels After the breakdown into the composition or moment of transition from the decomposition to the combination obtained from the types of sentences, the educator is used in 194 different wording types, and the sum of them is obtained by the amount of the advance of each wording state and the MAX value is considered as the output of the system. And at the end, the success rate of these methods and the effectiveness of these two types of labeling are compared with each other. Manuscript profile