LSTM-Based Telugu Text Generation Enhancing NLP for Content Creation and Language Preservation

Abstract

In this project, a Telugu text generator utilizing Long Short-Term Memory (LSTM) neural networks is developed to facilitate the automatic generation of coherent and contextually relevant Telugu text. The LSTM architecture, known for its effectiveness in handling sequential data, particularly in natural language processing tasks, serves as the foundation for this text generation system. The project aims to address the growing demand for computational tools capable of generating Telugu text, catering to various applications such as content creation, language learning, and cultural preservation. Through extensive training on a large dataset of Telugu text, the LSTM model learns the underlying patterns, semantics, and grammatical structures of the language, enabling it to generate text that closely resembles human-written Telugu content. The project's implementation involves data preprocessing, model training, and evaluation phases, with a focus on optimizing the LSTM network's performance in generating fluent and coherent Telugu text. The developed Telugu text generator provides a valuable resource for Telugu speakers, researchers, and developers interested in leveraging natural language processing techniques for Telugu language applications.

Index Terms

Telugu text generation, Long Short-Term Memory (LSTM) neural networks, Sequential data processing, Natural language processing (NLP), Computational linguistics, Text generation systems, Language modeling, Data preprocessing, Model training, Evaluation metrics, Grammatical structures, Semantic analysis, Cultural preservation, Language learning, Content creation, Telugu language applications, Fluency assessment, Coherence analysis, Text similarity, Resource optimization.