Applying the Markov chain in natural language processing and three-pool model

This paper delves into the application of Markov chains in Natural Language Processing (NLP), and the Markov Chain Monte Carlo (MCMC) methodology relevant to the three-pool model. The former outlines the basic principles of Markov chains, highlighting their utility in predicting word sequences in language modelling and text generation, despite certain limitations. Also, the former describes mathematical frameworks like n-gram models that enhance prediction accuracy by considering multiple preceding words. It acknowledges challenges in NLP such as oversimplification and emotional depth, as well as computational issues in higher-order models. It concludes by discussing the integration of Markov chains with other models to mitigate these limitations, and their enduring relevance in computational linguistics. The later investigates the MCMC methodology, a seminal development in the field of statistical inference, which is especially useful when analysing complicated systems when traditional statistical techniques are inadequate. Moreover, this later explores the fundamental concepts of MCMC, clarifies how it is inherently related to Markov chains, presents the three-pool model that is commonly applied to models of physical, chemical, or ecological systems, and discusses how MCMC can be used to analyse these models.