Merge pull request #115668 from diberry/diberry/0519-luis-machine-learned-2

[Cogsvcs] LUIS - machine-learned -> learning 2

Author: PRMerger18

articles/cognitive-services/LUIS/app-schema-definition.md

@@ -20,8 +20,8 @@ When you import and export the app, choose either `.json` or `.lu`.
 
 ## Version 7.x
 
-* Moving to version 7.x, the entities are represented as nested machine-learned entities.
-* Support for authoring nested machine-learned entities with `enableNestedChildren` property on the following authoring APIs:
+* Moving to version 7.x, the entities are represented as nested machine-learning entities.
+* Support for authoring nested machine-learning entities with `enableNestedChildren` property on the following authoring APIs:
     * [Add label](https://westus.dev.cognitive.microsoft.com/docs/services/luis-programmatic-apis-v3-0-preview/operations/5890b47c39e2bb052c5b9c08)
     * [Add batch label](https://westus.dev.cognitive.microsoft.com/docs/services/luis-programmatic-apis-v3-0-preview/operations/5890b47c39e2bb052c5b9c09)
     * [Review labels](https://westus.dev.cognitive.microsoft.com/docs/services/luis-programmatic-apis-v3-0-preview/operations/5890b47c39e2bb052c5b9c0a)
@@ -59,7 +59,7 @@ When you import and export the app, choose either `.json` or `.lu`.
 
 ## Version 6.x
 
-* Moving to version 6.x, use the new [machine-learned entity](reference-entity-machine-learned-entity.md) to represent your entities.
+* Moving to version 6.x, use the new [machine-learning entity](reference-entity-machine-learned-entity.md) to represent your entities.
 
 ```json
 {

articles/cognitive-services/LUIS/howto-add-prebuilt-models.md

@@ -73,7 +73,7 @@ The easiest way to view the value of a prebuilt model is to query from the publi
 
 ## Entities containing a prebuilt entity token
 
-If you have a machine-learned entity that needs a required feature of a prebuilt entity, add a subentity to the machine-learned entity, then add a _required_ feature of a prebuilt entity.
+If you have a machine-learning entity that needs a required feature of a prebuilt entity, add a subentity to the machine-learning entity, then add a _required_ feature of a prebuilt entity.
 
 ## Next steps
 > [!div class="nextstepaction"]

articles/cognitive-services/LUIS/label-entity-example-utterance.md

@@ -1,12 +1,12 @@
 ---
 title: Label entity example utterance
-description: Learn how to label a machine-learned entity with subcomponents in an example utterance in an intent detail page of the LUIS portal.
+description: Learn how to label a machine-learning entity with subcomponents in an example utterance in an intent detail page of the LUIS portal.
 ms.topic: conceptual
 ms.date: 05/17/2020
-#Customer intent: As a new user, I want to label a machine-learned entity in an example utterance.
+#Customer intent: As a new user, I want to label a machine-learning entity in an example utterance.
 ---
 
-# Label machine-learned entity in an example utterance
+# Label machine-learning entity in an example utterance
 
 Labeling an entity in an example utterance gives LUIS an example of what the entity is and where the entity can appear in the utterance.
 
@@ -33,7 +33,7 @@ Consider the example utterance, `hi, please I want a cheese pizza in 20 minutes`
 1. Select the left-most text, then select the right-most text of the entity, then from the in-place menu, pick the entity you want to label with.
 
     > [!div class="mx-imgBorder"]
-    > ![Label complete machine-learned entity](media/label-utterances/label-steps-in-place-menu.png)
+    > ![Label complete machine-learning entity](media/label-utterances/label-steps-in-place-menu.png)
 
 
 ## Label entity from Entity Palette
@@ -47,7 +47,7 @@ The entity palette offers an alternative to the previous labeling experience. It
 3. In the example utterance, _paint_ the entity with the cursor.
 
     > [!div class="mx-imgBorder"]
-    > ![Entity palette for machine-learned entity](media/label-utterances/example-1-label-machine-learned-entity-palette-label-action.png)
+    > ![Entity palette for machine-learning entity](media/label-utterances/example-1-label-machine-learned-entity-palette-label-action.png)
 
 ## Adding entity as a feature from the Entity Palette
 
@@ -70,7 +70,7 @@ Entity roles are labeled using the **Entity palette**.
 After labeling, review the example utterance and ensure the selected span of text has been underlined with the chosen entity. The solid line indicates the text has been labeled.
 
 > [!div class="mx-imgBorder"]
-> ![Labeled complete machine-learned entity](media/label-utterances/example-1-label-machine-learned-entity-complete-order-labeled.png)
+> ![Labeled complete machine-learning entity](media/label-utterances/example-1-label-machine-learned-entity-complete-order-labeled.png)
 
 ## Confirm predicted entity
 
@@ -109,7 +109,7 @@ Non-machine learned entities include prebuilt entities, regular expression entit
 Entity prediction errors indicate the predicted entity doesn't match the labeled entity. This is visualized with a caution indicator next to the utterance.
 
 > [!div class="mx-imgBorder"]
-> ![Entity palette for machine-learned entity](media/label-utterances/example-utterance-indicates-prediction-error.png)
+> ![Entity palette for machine-learning entity](media/label-utterances/example-utterance-indicates-prediction-error.png)
 
 ## Next steps
 

articles/cognitive-services/LUIS/luis-concept-app-iteration.md

@@ -110,7 +110,7 @@ A version can be exported at the app or version level as well. The only differen
 
 The exported file **doesn't** contain:
 
-* Machine-learned information, because the app is retrained after it's imported
+* machine-learning information, because the app is retrained after it's imported
 * Contributor information
 
 In order to back up your LUIS app schema, export a version from the [LUIS portal](https://www.luis.ai/applications).

articles/cognitive-services/LUIS/luis-concept-batch-test.md

@@ -19,7 +19,7 @@ Batch testing validates your active trained version to measure its prediction ac
 
 ## Group data for batch test
 
-It is important that utterances used for batch testing are new to LUIS. If you have a data set of utterances, divide the utterances into three sets: example utterances added to an intent, utterances received from the published endpoint, and utterances used to batch test LUIS after it is trained. 
+It is important that utterances used for batch testing are new to LUIS. If you have a data set of utterances, divide the utterances into three sets: example utterances added to an intent, utterances received from the published endpoint, and utterances used to batch test LUIS after it is trained.
 
 ## A data set of utterances
 
@@ -30,7 +30,7 @@ Submit a batch file of utterances, known as a *data set*, for batch testing. The
 |*No duplicate utterances|
 |1000 utterances or less|
 
-*Duplicates are considered exact string matches, not matches that are tokenized first. 
+*Duplicates are considered exact string matches, not matches that are tokenized first.
 
 ## Entities allowed in batch tests
 
@@ -41,7 +41,7 @@ All custom entities in the model appear in the batch test entities filter even i
 
 ## Batch file format
 
-The batch file consists of utterances. Each utterance must have an expected intent prediction along with any [machine-learned entities](luis-concept-entity-types.md#types-of-entities) you expect to be detected. 
+The batch file consists of utterances. Each utterance must have an expected intent prediction along with any [machine-learning entities](luis-concept-entity-types.md#types-of-entities) you expect to be detected.
 
 ## Batch syntax template for intents with entities
 
@@ -52,7 +52,7 @@ Use the following template to start your batch file:
   {
     "text": "example utterance goes here",
     "intent": "intent name goes here",
-    "entities": 
+    "entities":
     [
         {
             "entity": "entity name 1 goes here",
@@ -69,7 +69,7 @@ Use the following template to start your batch file:
 ]
 ```
 
-The batch file uses the **startPos** and **endPos** properties to note the beginning and end of an entity. The values are zero-based and should not begin or end on a space. This is different from the query logs, which use startIndex and endIndex properties. 
+The batch file uses the **startPos** and **endPos** properties to note the beginning and end of an entity. The values are zero-based and should not begin or end on a space. This is different from the query logs, which use startIndex and endIndex properties.
 
 [!INCLUDE [Entity roles in batch testing - currently not supported](../../../includes/cognitive-services-luis-roles-not-supported-in-batch-testing.md)]
 
@@ -92,7 +92,7 @@ If you do not want to test entities, include the `entities` property and set the
 
 ## Common errors importing a batch
 
-Common errors include: 
+Common errors include:
 
 > * More than 1,000 utterances
 > * An utterance JSON object that doesn't have an entities property. The property can be an empty array.
@@ -107,7 +107,7 @@ LUIS tracks the state of each data set's last test. This includes the size (numb
 
 ## Batch test results
 
-The batch test result is a scatter graph, known as an error matrix. This graph is a 4-way comparison of the utterances in the batch file and the current model's predicted intent and entities. 
+The batch test result is a scatter graph, known as an error matrix. This graph is a 4-way comparison of the utterances in the batch file and the current model's predicted intent and entities.
 
 Data points on the **False Positive** and **False Negative** sections indicate errors, which should be investigated. If all data points are on the **True Positive** and **True Negative** sections, then your app's accuracy is perfect on this data set.
 
@@ -119,13 +119,13 @@ This chart helps you find utterances that LUIS predicts incorrectly based on its
 
 ## Errors in the results
 
-Errors in the batch test indicate intents that are not predicted as noted in the batch file. Errors are indicated in the two red sections of the chart. 
+Errors in the batch test indicate intents that are not predicted as noted in the batch file. Errors are indicated in the two red sections of the chart.
 
-The false positive section indicates that an utterance matched an intent or entity when it shouldn't have. The false negative indicates an utterance did not match an intent or entity when it should have. 
+The false positive section indicates that an utterance matched an intent or entity when it shouldn't have. The false negative indicates an utterance did not match an intent or entity when it should have.
 
 ## Fixing batch errors
 
-If there are errors in the batch testing, you can either add more utterances to an intent, and/or label more utterances with the entity to help LUIS make the discrimination between intents. If you have added utterances, and labeled them, and still get prediction errors in batch testing, consider adding a [phrase list](luis-concept-feature.md) feature with domain-specific vocabulary to help LUIS learn faster. 
+If there are errors in the batch testing, you can either add more utterances to an intent, and/or label more utterances with the entity to help LUIS make the discrimination between intents. If you have added utterances, and labeled them, and still get prediction errors in batch testing, consider adding a [phrase list](luis-concept-feature.md) feature with domain-specific vocabulary to help LUIS learn faster.
 
 ## Next steps
 

articles/cognitive-services/LUIS/luis-concept-best-practices.md

@@ -95,9 +95,9 @@ Model decomposition has a typical process of:
 
 Once you have created the intent and added example utterances, the following example describes entity decomposition.
 
-Start by identifying complete data concepts you want to extract in an utterance. This is your machine-learned entity. Then decompose the phrase into its parts. This includes identifying subentities, and features.
+Start by identifying complete data concepts you want to extract in an utterance. This is your machine-learning entity. Then decompose the phrase into its parts. This includes identifying subentities, and features.
 
-For example if you want to extract an address, the top machine-learned entity could be called `Address`. While creating the address, identify some of its subentities such as street address, city, state, and postal code.
+For example if you want to extract an address, the top machine-learning entity could be called `Address`. While creating the address, identify some of its subentities such as street address, city, state, and postal code.
 
 Continue decomposing those elements by:
 * Adding a required feature of the postal code as a regular expression entity.
@@ -152,7 +152,7 @@ After the app is published, only add utterances from active learning in the deve
 
 ## Don't use few or simple entities
 
-Entities are built for data extraction and prediction. It is important that each intent have machine-learned entities that describe the data in the intent. This helps LUIS predict the intent, even if your client application doesn't need to use the extracted entity.
+Entities are built for data extraction and prediction. It is important that each intent have machine-learning entities that describe the data in the intent. This helps LUIS predict the intent, even if your client application doesn't need to use the extracted entity.
 
 ## Don't use LUIS as a training platform
 

articles/cognitive-services/LUIS/luis-concept-data-extraction.md

@@ -9,7 +9,7 @@ ms.date: 05/01/2020
 # Extract data from utterance text with intents and entities
 LUIS gives you the ability to get information from a user's natural language utterances. The information is extracted in a way that it can be used by a program, application, or chat bot to take action. In the following sections, learn what data is returned from intents and entities with examples of JSON.
 
-The hardest data to extract is the machine-learned data because it isn't an exact text match. Data extraction of the machine-learned [entities](luis-concept-entity-types.md) needs to be part of the [authoring cycle](luis-concept-app-iteration.md) until you're confident you receive the data you expect.
+The hardest data to extract is the machine-learning data because it isn't an exact text match. Data extraction of the machine-learning [entities](luis-concept-entity-types.md) needs to be part of the [authoring cycle](luis-concept-app-iteration.md) until you're confident you receive the data you expect.
 
 ## Data location and key usage
 LUIS extracts data from the user's utterance at the published [endpoint](luis-glossary.md#endpoint). The **HTTPS request** (POST or GET) contains the utterance as well as some optional configurations such as staging or production environments.
@@ -240,7 +240,7 @@ Some apps need to be able to find new and emerging names such as products or com
 
 ## Pattern.any entity data
 
-[Pattern.any](reference-entity-pattern-any.md) is a variable-length placeholder used only in a pattern's template utterance to mark where the entity begins and ends. The entity used in the pattern must be found in order for the pattern to be applied. 
+[Pattern.any](reference-entity-pattern-any.md) is a variable-length placeholder used only in a pattern's template utterance to mark where the entity begins and ends. The entity used in the pattern must be found in order for the pattern to be applied.
 
 ## Sentiment analysis
 If Sentiment analysis is configured while [publishing](luis-how-to-publish-app.md#sentiment-analysis), the LUIS json response includes sentiment analysis. Learn more about sentiment analysis in the [Text Analytics](https://docs.microsoft.com/azure/cognitive-services/text-analytics/) documentation.