Set publishing configuration options
When you publish a Language Understanding app, you can select various publishing options.
Publishing slot
Every Language Understanding app has two publishing slots:
- Staging. Use this slot to publish and test new versions of your language model without disrupting production applications.
- Production. Use this slot for "live" models that are used by production applications.
Publish settings
Regardless of which slot you publish your Language Understanding app to, you can configure the following publish settings to enable specific behavior:
- Sentiment analysis. Enable this to include a sentiment score from 0 (negative) to 1 (positive) in predictions. This score reflects the sentiment of the input utterance.
- Spelling correction. Enable this to use the Bing Spell Check service to correct the spelling on input utterances before intent prediction.
- Speech priming. Enable this if you plan to use the language model with the Speech service. This option sends the model to the Speech service ahead of prediction to improve intent recognition from spoken input.
Process predictions
To consume your Language Understanding model in a client application, you can use the REST APIs or one of the programming language-specific SDKs.
Regardless of the approach used, requests for predictions are sent to a published slot (production or staging) and include the following parameters:
- query - the utterance text to be analyzed.
- show-all-intents - indicates whether to include all identified intents and their scores, or only the most likely intent.
- verbose - used to include additional metadata in the results, such as the start index and length of strings identified as entities,
- log used to record queries and results for use in Active Learning.
Prediction results
The prediction results consist of a hierarchy of information that your application must parse. When using the REST interface, the results are in JSON form. SDKs present the results as an object hierarchy based on the underlying JSON.
A typical response might look similar to this:
{
"query": "What's the time in Edinburgh?",
"prediction": {
"topIntent": "GetTime",
"intents": {
"GetTime": {
"score": 0.9978
},
...
},
"entities": {
"location": ["Edinburgh"],
...
}
}
}
The prediction results include the query utterance, the top (most likely) intent along with its confidence score, and the entities that were detected; which are provided as an object for each entity (for example location) with a list of the instances of that entity that were detected (for example, "Edinburgh"). Depending on the options specified in the request, the results may also include any other intents that were identified as being a possible match, and details about the location of each entity in the utterance string.
Note
It's important to emphasize that the Language Understanding service enables your application to identify the intent of the user (in this case to find out the current time in Edinburgh). It is the responsibility of the client application to then perform whatever logic is necessary to fulfill the intent (so the Language understanding model does not return the actual time in Edinburgh - it simply indicates to the client application that this is the information that the user wants.)