Dima Suleiman, Arafat Awajan, and Wael Al Etaiwi. 2017. The Use of Hidden Markov Model in Natural ARABIC Language Processing: a survey. Procedia Computer Science 113, (2017), 240–247. DOI: https://doi.org/10.1016/j.procs.2017.08.363


Hidden  Markov  Model  is  an  empirical  tool  that  can  be  used  in  many  applications  related  to  natural  language  processing.  In  this  paper  a  comparative  study  was  conducted  between  different  applications  in  natural  Arabic  language  processing  that  uses  Hidden  Markov  Model  such  as  morphological  analysis,  part  of  speech  tagging, text classification, and name entity recognition. Comparative results showed that HMM can be used in different layers of natural  language  processing,  but  mainly  in  pre-processing  phase  such  as:  part  of  speech  tagging,  morphological  analysis and syntactic structure;  however in high level applications  text classification their use is limited to certain number of researches.