It's not as complex to build your own chatbot (or assistant, this word is a new trendy term for a chatbot) as you may think. Various chatbot platforms are using classification models to recognize user intent. While obviously, you get a strong heads-up when building a chatbot on top of the existing platform, it never hurts to study the background concepts and try to build it yourself. Why not use a similar model yourself? The main challenges of chatbot implementation are:
- Classify user input to recognize intent (this can be solved with machine learning, I'm using Keras with TensorFlow backend)
- Keep context. This part is programming, and there is nothing much ML-related here. I'm using the Node.js backend logic to track conversation context (while in context, we typically don't require a classification for user intents — user input is treated as answers to chatbot questions)
Complete source code for this article with readme instructions is available on my GitHub repo (open source).
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