TamilCogniBERT: Enhancing Tamil Language Comprehension using Self Learning
G Ashwinraj, Sarfraz Hussain M, Madhu Perkin T · 2024
TamilCogniBERT is a ground-breaking initiative that uses cutting-edge natural language processing methods to improve comprehension of the Tamil language. The research uses a leading deep learning model called BERT (Bidirectional Encoder Representations from Transformers) to create a powerful language comprehension system designed for Tamil. TamilCogniBERT aims to close the gap in language processing technology for Tamil, facilitating more efficient communication and understanding in this complex and multifaceted language. The project comprises several essential elements, such as gathering data, preprocessing, modifying the model, incorporating user input, and implementing self-learning processes. First, a large-scale corpus of Tamil text data is carefully selected from a variety of internet, offline, and social media sources. Tokenization, noise reduction, and normalization are just a few of the rigorous preparation procedures this corpus goes through to guarantee quality and consistency. Subsequently, the Tamil corpus is utilized to modify and enhance a pre-trained BERT model. This technique entails using tasks like text categorization, sentiment analysis, and question answering to train the model on information pertinent to Tamil language understanding. The model is now capable of correctly comprehending and interpreting Tamil text thanks to this update. TamilCogniBERT's use of user feedback techniques is one of its unique advantages. The model learns to be more sensitive to the subtleties and complexities of the Tamil language as it is used in everyday situations by asking users for feedback and incorporating their responses into the learning process. TamilCogniBERT enhances the training process by using self-learning strategies.