Abstract
Markov chain theory isan important tool in applied probability that is quite useful in modeling
real-world computing applications.For a long time, rresearchers have used Markov chains for
data modeling in a wide range of applications that belong to different fields such as
computational linguists, image processing, communications,bioinformatics, finance systems,
etc. This paper explores the Markov chain theory and its extension hidden Markov models
(HMM) in natural language processing (NLP) applications. This paper also presents some
aspects related to Markov chains and HMM such as creating transition matrices, calculating
data sequence probabilities, and extracting the hidden states.
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