Exploring LSTM-Based Text Generation with Historical Datasets

This study explores the effectiveness of LSTM networks in text generation using historical datasets, showcasing high accuracy and efficiency in predicting text from Shakespeare and Nietzsche's works. The author argues that LSTM-based models trained on historical datasets can generate linguistically rich and contextually relevant text, offering insights into language pattern evolution over time.