期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2017
卷号：7
期号：1
页码：25
DOI：10.5121/ijdkp.2017.7103
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Markov chain theory is a popular statistical tool in applied probability that is quite useful in modellingreal-world computing applications. Over the past years; there has been grown interest to employ Markovchain theory in statistical learning of temporal (i.e. time series) data. A wide range of applications found toutilize Markov concepts; such applications include computational linguists, image processing,communications, bioinformatics, finance systems, etc .In fact, Markov processes based research appliedwith great success in many of the most efficient natural language processing (NLP) tools. Hence, this paperexplores the Markov chain theory and its extension hidden Markov models (HMM) in (NLP) applications.This paper also presents some aspects related to Markov chains and HMM such as creating transition andobservation matrices, calculating data sequence probabilities, extracting the hidden states, and profileHMM.
