A Stochastic Approach to Identify POS in Iraqi National Song using N-Iterative HMM using Agile Approach

Abstract

This research article conveys the use of an agile approach for building an N-iterative HMM model for POST (Part of Speech Tagging) analysis. The agile model is a phenomenon or approach which has vast application. The implementation of such an iterative model is discussed in this paper. Most effectively, the information is conveyed with the help of the exact word we use during the communication. The sentence may not be a complete sentence or grammatically correct sentence, but the purpose of communication is served without any hurdle. Ages witnessing the evolution of the language earlier medium was sign language that might not contain any language communication rules that can be completed. Now complete language with so many tools and API is available, but the way preferred for the processing is the same (logically). The designed iterations lead to improved quality validation by using the word by word score calculation. The experiment is conducted to complete Part of Speech Tagging (POST) for Iraqi National Song (data set of translated Iraqi national song), and the stochastic approach used for completing the tagging is Iterative Hidden Markov Model. The results of the experiments convey the positive impact of the iterative approach over accuracy.

Highly Efficient Parts of Speech Tagging in Low Resource Languages with Improved Hidden Markov Model and Deep Learning

“…al(2021) have used an N iterative HMM model for parts of speech tagging in Iraqi National Song. The iterative approach has improved accuracy as claimed by the authors [14]. Since HMM uses a huge amount of training data, it can very well map the context of the words and provide high accuracy as seen in the works conducted by the researcher discussed above.…”