LSTM-Based Text Generation: A Study on Historical Datasets
Mustafa Abbas Hussein Hussein Serkan Savaş
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 also provide
insights into the evolution of language patterns over time. The finding
presents models that are highly accurate and efficient in predicting text from
works of Nietzsche, with low loss values and a training time of 100 iterations.
The accuracy of the model is 0.9521, indicating high accuracy. The loss of the
model is 0.2518, indicating its effectiveness. The accuracy of the model in
predicting text from the work of Shakespeare is 0.9125, indicating a low error
rate. The training time of the model is 100, mirroring the efficiency of the
Nietzsche dataset. This efficiency demonstrates the effectiveness of the model
design and training methodology, especially when handling complex literary
texts. This research contributes to the field of natural language processing by
showcasing the versatility of LSTM networks in text generation and offering a
pathway for future explorations in historical linguistics and beyond.