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Chatbots - Coggle Diagram
Chatbots
Entity
The extracted data is called a parameter or entity type. Parameters and entity types are structured data that can help us make better sense of what the user is trying to find out.
System entities - e.g. dates, times, numbers, email addresses, etc.
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Intent
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Dialogflow matches the end-user expression to the best intent in your agent. Matching an intent is also known as intent classification.
You can define mutiple intents for a single agent, just like how a call centre agent can decide if you need basic troubleshooting, make an appointment, or redirect your phone calls based on what you say
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Chatbots can be built through different platforms, such as via IBM Watson Assistant, or Google Dialogflow
Dialogflow
Dialogflow uses Natural Language Processing (NLP), which is a branch under artificial intelligence.
In particular, NLP is how we can train computers to understand and process the natural human language.
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Agent
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A Dialogflow agent is similar to a human call centre agent. Whether it is a Dialogflow agent or a human agent, both need to be trained to handle conversational scenarios. Training does not need to be overly explicit.
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How Dialogflow Works
A Dialogflow conversation begins from the (1) end-user expression, have (2) its intent matched by the agent with the use of training phrases and entities, before (3) response is being provided back to the user.
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In Dialogflow, you should add no more than 4 training phrases per intent.
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Which tab in Dialogflow allows us to view end-user expressions to validate the matched intent or reassign intents?
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