The research shows that one of the most effective artificial neural network types for Natural Language Processing tasks is Recurrent Neural Networks (RNNs). RNNs are widely used in NLP tasks such as machine translation, text generation, image captioning. In NLP tasks, we usually use NLP tools and methods to process the text data into vectors and then feed them into a selected artificial neural network such as RNN, CNN, or even a feedforward neural network to complete a task. An RNN can generate text from words as well as from characters, and we select to use characters to generate text for this case study. When we build a new RNN with no training, it combines a bunch of meaningless characters, which does not mean anything. However, if we feed our RNN with a lot of text data, it starts to imitate these texts' style and generate meaningful text using characters. So, if we feed the model a lot of didactic text, our model would generate educational materials. If we feed our model with lots of poems, our model will generate poems, so we would have an artificial poet. These are all viable options, but we will feed our model with something else: A long text dataset containing Shakespeare’s writings. Therefore, we will create a Shakespeare bot.
dineshssdn-867/AI_Shakespeare
The research shows that one of the most effective artificial neural network types for Natural Language Processing tasks is Recurrent Neural Networks (RNNs). RNNs are widely used in NLP tasks such as machine translation, text generation, image captioning. In NLP tasks, we usually use NLP tools and methods to process the text data into vectors and then feed them into a selected artificial neural network such as RNN, CNN, or even a feedforward neural network to complete a task.
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