An Automated Conversation System Using Natural Language Processing (NLP) Chatbot In Python - Info and Reading Options
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<span style="color:rgb(119,119,119);font-family:Georgia, 'Times New Roman', Times, serif;font-size:16.5px;text-align:justify;background-color:rgb(255,255,255);">The purpose of this project is to build a ChatBot that utilises NLP (Natural Language Processing) and assists customers. A ChatBot is an automated conversation system that replies to users' queries by analysing them using NLP and assists them in every way it can. In this project, we are trying to implement a customer service chatbot that tries to converse and assist the user in some simple scenarios. This chat bot can take simple user queries as input, process them, classify them into one of the existing tags, and respond to them with an appropriate response. If the user's queries are too complex for the bot, it will re-direct the conversation to an actual person. The ChatBot is going to be based on a machine learning model that is built using PyTorch (Python Deep Learning library) and NLTK (Natural Language Tool Kit). The model used here is a feed-forward neural network. There are 3 layers in this neural network, i.e., the input layer, the hidden layer, and the output layer. The number of nodes in the input and hidden layers depends on the total number of distinct words present in the data set. whereas the output contains the same number of nodes as the number of distinct tags the data set is divided into. This kind of neural network is perfect for building simple chatbots as it does not require high computational power either for training or for deploying. The chatbot we built is for a coffee shop, and it performs actions like ordering coffee, telling a joke, suggesting a drink, etc. Although this chatbot is relatively simple, it is highly customizable, thus making it easy to implement it in any scenario.One of the main features of this ChatBot is that the dataset it is trained on is easy to customise and we can add new tags easily, but the neural network need not be altered in most cases, making this a very reliable model. Many chatbots similar to this are being used in fields like medicine, government agencies, automated food ordering systems, etc. This feature also makes training and testing the chatbot very easy to customize.</span>
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