Number prebuilt entity for a LUIS app

There are many ways in which numeric values are used to quantify, express, and describe pieces of information. This article covers only some of the possible examples. LUIS interprets the variations in user utterances and returns consistent numeric values. Because this entity is already trained, you do not need to add example utterances containing number to the application intents.

Types of number

Number is managed from the Recognizers-text GitHub repository

Examples of number resolution

Utterance Entity Resolution
one thousand times "one thousand" "1000"
1,000 people "1,000" "1000"
1/2 cup "1 / 2" "0.5"
one half the amount "one half" "0.5"
one hundred fifty orders "one hundred fifty" "150"
one hundred and fifty books "one hundred and fifty" "150"
a grade of one point five "one point five" "1.5"
buy two dozen eggs "two dozen" "24"

LUIS includes the recognized value of a builtin.number entity in the resolution field of the JSON response it returns.

Resolution for prebuilt number

The following entity objects are returned for the query:

order two dozen eggs

The following JSON is with the verbose parameter set to false:

"entities": {
    "number": [
        24
    ]
}

Next steps

Learn more about the V3 prediction endpoint.

Learn about the currency, ordinal, and percentage.