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Chatbots - Coggle Diagram
Chatbots
Why use chatbots?
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With the use of chatbots, manpower can be re-allocated to areas of work which cannot be automated
What was a 10-minute interaction between a customer and a service agent, is now a 2-minute conversation between the customer and a chatbot with a name and face. The service agent is now able to focus on other tasks.
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Dialogflow
Dialogflow uses Natural Language Processing (NLP), which is a branch under artificial intelligence.
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Agents, Intents and Entities in Dialogflow
Agents
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Once a user sends a text message to the Dialogflow agent, Google uses artificial intelligence to translate the message into structured data with the aid of NLP
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Intent
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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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Entity
When an intent is matched, Dialogflow can extract specific information from the end-user expression. 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
In Dialogflow, there are system entities - e.g. dates, times, numbers, email addresses, etc
You can also create your own custom entities. For example, you could define a vegetable entity that can match the types of vegetables available for purchase with a grocery store agent
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.