Learn Terms in Wit.AI

Before we train our Wit app, we should learn about intents, entities, traits, and utterances. If you already learn those terms, you can go to Next section

Case Study: We want to understand what our end-user wants to perform. For example:

Ask about the weather
Book a restaurant
Open the garage door

There are a millions of different ways to express a given intent. For instance, all the following expressions should be mapped to the same intent:

"What is the weather in Paris?" "Give me tomorrow's weather in Paris." "Is it sunny or rainy in Paris now?"

Those expressions are asking about the weather intent. How about entities ? Entities are object that referred in the intent of sentence.

"What is the weather in Paris ?" Paris is a city where we ask about the weather.

"Give me tomorrow weather in Paris." Tomorrow is time when we ask about the weather for.

"Is it sunny or rainy in Paris now?" And sunny and rainy are options what we ask about the weather.

The entities make machine understand what object that related with the intent. example: "Give me tomorrow weather in Paris."

Intent: Ask about the weather , Entities: { City: Paris Time: Tomorrow } Machine could query to the database in table weather(intent) with paris city and tomorrow queries (entities)

So what is trait ? Trait is tendency of an intent. We could give an example of this like sentiment on reaction_intent.

"Sad" (negative) "OMG :(" (negative) "I can't believe this. I'm crying" (negative) "Superb" (positive)

Utterances are sample data which define a sentence to be categorized to an intent and have entities and traits. This term will be used to train data, for example:

pic utterance

Now that we understand, let’s train our Wit app to process the user’s response to the app.