Word sense disambiguation of regional language using deep learning

India is widely renowned for having many different languages. Indians must be quite proud of the rich diversity of languages in their country. As there are numerous languages and a large number of meaningful words. There are terms that have synonyms in every language. There will be a time when learning a new language is necessary. Natural language processing (NLP) is helpful in achieving such. It has been around for more than 50 years, and the roots of NLP may be found in the study of language. It is used in a variety of fields, including corporate intelligence, search engines, and medical research. Ambiguity is the quality of being subject to multiple interpretations, and is frequently referred to as the imprecision of a word's meaning. The method for fixing this problem is called disambiguation. This study used a large- scale dataset that included words with many meanings and senses. Additionally, the dataset is in Telugu, the regional language of Andhra Pradesh. Some of the Telugu words in this dataset are unclear when used in various contexts. Deep neural networks are utilized to do this. Two algorithms namely Bidirectional Long Short Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (RBGRU) are used in order to obtain the noticeable sense of given ambiguous word. Accurate word sense prediction achieved an accuracy of 86.30% for word sense disambiguation for regional language results. This outcome is remarkable when compared with other approaches of word sense prediction of various regional languages