Talal Almutiri

Work place: Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

E-mail: almutiri.talal@hotmail.com


Research Interests: Computational Learning Theory, Natural Language Processing, Image Processing, Data Structures and Algorithms


Talal Almutiri is a PhD student at the Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, KSA. He received his BSc and MSc in Information Systems from Taibah University. His research interests are machine learning, natural language processing (NLP), and bioinformatics, and he has published different papers in his interest domains.

Author Articles
Markov Models Applications in Natural Language Processing: A Survey

By Talal Almutiri Farrukh Nadeem

DOI: https://doi.org/10.5815/ijitcs.2022.02.01, Pub. Date: 8 Apr. 2022

Markov models are one of the widely used techniques in machine learning to process natural language. Markov Chains and Hidden Markov Models are stochastic techniques employed for modeling systems that are dynamic and where the future state relies on the current state.  The Markov chain, which generates a sequence of words to create a complete sentence, is frequently used in generating natural language. The hidden Markov model is employed in named-entity recognition and the tagging of parts of speech, which tries to predict hidden tags based on observed words. This paper reviews Markov models' use in three applications of natural language processing (NLP): natural language generation, named-entity recognition, and parts of speech tagging. Nowadays, researchers try to reduce dependence on lexicon or annotation tasks in NLP. In this paper, we have focused on Markov Models as a stochastic approach to process NLP. A literature review was conducted to summarize research attempts with focusing on methods/techniques that used Markov Models to process NLP, their advantages, and disadvantages. Most NLP research studies apply supervised models with the improvement of using Markov models to decrease the dependency on annotation tasks. Some others employed unsupervised solutions for reducing dependence on a lexicon or labeled datasets.

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