Generative AI for Text Generation with Word Estimation Module for the Natural Language Processing
Biswanath Saha · Journal of Computer Allied Intelligence (JCAI). · 2025
Generative AI has made significant strides in Natural Language Processing (NLP), especially in the domain of text generation. NLP - text creation technology based on Generative Adversarial Networks (GANs) utilizes AI to compose original NLP-text by training two neural networks – a generator and a discriminator – to work together. The generator creates new NLP-text samples, while the discriminator evaluates them against real NLP-text data, pushing the generator to improve until it produces convincing, high-quality compositions. This technology enables the generation of diverse, innovative NLP - text styles that can mimic specific genres or create entirely new sounds, offering exciting possibilities for NLP-text, producers, and the entertainment industry to experiment with fresh and unique NLP-text content. This paper explores the application of Generative Adversarial Networks (GANs) coupled with a Words Estimation Module (PEM) for NLP - text generation. The GAN-PEM model is designed to generate high-quality NLP-text compositions by incorporating words estimation algorithms to enhance NLP - text. Through a series of experiments, we investigate the model's proficiency in words estimation accuracy, genre prediction, and loss estimation. The results demonstrate that the GAN-PEM model consistently achieves high levels of accuracy in words estimation, with an average accuracy of 93%. Additionally, the model exhibits robustness and versatility in capturing intricate NLP - text patterns and structures, showcasing its potential for creative exploration in NLP - text composition. The results demonstrated that At Iteration 1, the generator loss was 1.20, with words accuracy at 60%, spectral smoothness of -12.5 dB, and rhythmic accuracy of 50%. The melodic diversity was 3, with a realism score of 4 and a text structure score of 3, indicating early-stage performance with significant room for improvement. By Iteration 10, the generator loss decreased to 0.75, words accuracy increased to 75%, and spectral smoothness improved to -14.2 dB. Rhythmic accuracy improved to 60%, melodic diversity reached 4, and both realism and text structure scores rose to 6 and 5, respectively. In Iteration 50, the model achieved words accuracy of 85%, spectral smoothness of -18.3 dB, and rhythmic accuracy of 80%. The melodic diversity increased to 7, with dynamic range at 16 dB, and both the realism and text structure scores improved to 8. By Iteration 100, words accuracy reached 90%, spectral smoothness improved to - 20.4 dB, and accuracy reached 88%. The melodic diversity was 8, the dynamic range increased to 18 dB, and realism and text structure scores rose to 9.