Classification – Machine Learning Chatbot with TensorFlow by Andrejus Baranovskis

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Visual conversation flow is a first thing to create, when you want to build chatbot. Such flow will help to define proper set of intents along with dialog path. Otherwise it is very easy to get lost in conversation transitions and this will lead to chatbot implementation failure. Our chatbot for medical system doesn’t make any decisions, instead it helps user to work with enterprise system. It gets user input and during conversation leads to certain API call – which at the end triggers enterprise system to execute one or another action. If user is looking for patient blood pressure results, chatbot will open blood pressure module with patient ID. If user wants to edit or review blood pressure results in general, chatbot will load blood pressure results module without parameters. This kind of chatbot is very helpful in large and complex enterprise systems, this helps to onboard new users much quicker without extra training for system usage. Example of visual conversation flow for chatbot:

Conversation intents can be logged in JSON file. Where you should list conversation patterns mapped with tags, responses and contextual information. Chatbot is not only about machine learning and user input processing, very important is to handle conversation contextual flow and usually this is done outside of machine learning area in another module. We will look into it later. Machine learning with neural network is responsible to allow chatbot to calculate tag probability based on user input. In other words – machine learning helps to bring the best matching tag for current sentence, based on predefined intents patterns. As long as we get probability for the intent tag – we know what user wants, we can set conversation context and in the next user request – react based on current context: Read the complete article here.

 

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