Abstract
Text Generation has become one of the functional areas of Natural Language Processing (NLP) and is considered a major advancement in this generation of creative texts such as literature. Every year, the field is receiving increased future research. With the advent of Machine Learning and the Deep Neural Network, it has become possible to process and find hidden patterns from huge amounts of information (Big Data). Our research is focused on the use of the Neural Network Model to construct an AI assistant for writers, primarily poets, using Bi-Directional Long Short-Term Memory (LSTM), a variant of the Recurrent Neural Network (RNN), to generate poetry in the English language by providing it with a huge corpus of poems compiled specifically for research by renowned poets. With two unique themes, love and nature, the dataset is specifically chosen. Most of the poems are from the Renaissance and Modern period. This method will function in two ways, firstly by creating full-length poems and sonnets, and secondly as a provocative tool for the writer to make it a hybrid system in which a creative piece of work is produced by both the writer and AI. Some issues with previous versions and models have been improved, such as lack of coherence and rhyme, along with a newly designed use case introduced in our report, such as AI assistant. This model can produce unique poetry along with providing meaningful suggestions with a suggestion of rhyming with appropriate precision in some outputs.
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Ratawal, Y., Makhloga, V.S., Raheja, K., Chadha, P., Bhatt, N. (2022). PoemAI: Text Generator Assistant for Writers. In: Raj, J.S., Palanisamy, R., Perikos, I., Shi, Y. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 213. Springer, Singapore. https://doi.org/10.1007/978-981-16-2422-3_45
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