The Natural Language Processing Axioms in Classical Tamil for Zonal Dialects using Machine Learning

Abstract

Studying Natural Language Processing (NLP) for Classical Tamil and its Zonal Dialects using Machine Learning (ML) involves unique challenges and opportunities. Classical Tamil, being one of the oldest languages with a rich literary heritage, differs significantly in syntax, semantics, and phonetics from its modern dialects. Addressing these differences requires incorporating linguistic axioms and cultural nuances into NLP systems. This deals with Tamil letters, Challenges, Future directions, and Lexical Differences. It also includes parsers, tokenisation. Lists the differences between Morphemes, Bounded Morphemes in terms of Tamil as a Natural Language processing—dictionary form of the words used in Lemmatizations. Stemming is used to reduce the words and Tamil represented as a short sentence. Comparison of differences in the dialect of Tamil taken and represented. The methodology used is Clustering algorithms can group zonal dialects based on phonetic and semantic similarities using a Naïve Bayes classifier. We are using Speech to Text for identifying the Tamil dialect. This zonal dialect plays an important role in Entertainment, education, Information, and Business purposes. More Exploration can be done using Zonal dialects in Classical Tamil. Machine learning plays a role in classification, Grouping, and Segmenting Natural Language processing. For a single word in Natural Language processing, we have different dialects in the Single Language Tamil. Encourages local people to communicate fluently in terms of transactions. Preserving local traditions and customs is the advantage of Zonal Dialects. It can be used in interviews, recordings, written and spoken texts, and debates. Linguistic Diversity, preservation of History, and Cultural Identity are the major concerns in the field of Zonal dialects using classical Tamil.