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LSTM-Based Text Generation: A Study on Historical Datasets
March 13, 2024, 4:46 a.m. | Mustafa Abbas Hussein Hussein, Serkan Sava\c{s}
cs.CL updates on arXiv.org arxiv.org
Abstract: This paper presents an exploration of Long Short-Term Memory (LSTM) networks in the realm of text generation, focusing on the utilization of historical datasets for Shakespeare and Nietzsche. LSTMs, known for their effectiveness in handling sequential data, are applied here to model complex language patterns and structures inherent in historical texts. The study demonstrates that LSTM-based models, when trained on historical datasets, can not only generate text that is linguistically rich and contextually relevant but …
abstract arxiv cs.ai cs.cl data datasets exploration language long short-term memory lstm memory networks nietzsche paper patterns study text text generation type
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