| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 4960717 | Procedia Computer Science | 2017 | 8 Pages |
Abstract
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.
Keywords
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Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Dima Suleiman, Arafat Awajan, Wael Al Etaiwi,