[Cogsvcs] LUIS machine-learned -> learning 4

Author: diberry

articles/cognitive-services/LUIS/includes/decomposable-tutorial-links.md

@@ -9,9 +9,9 @@ ms.date: 05/06/2020
 ms.author: diberry
 ---
 
-To take advantage of [decomposable concepts](../luis-concept-model.md#v3-authoring-model-decomposition), start with the machine-learned entity. Follow the [machine-learned entity tutorial](../tutorial-machine-learned-entity.md) for steps on how to create this type of entity including examples of:
+To take advantage of [decomposable concepts](../luis-concept-model.md#v3-authoring-model-decomposition), start with the machine-learning entity. Follow the [machine-learning entity tutorial](../tutorial-machine-learned-entity.md) for steps on how to create this type of entity including examples of:
 
-* [Create and label machine-learned entity](../tutorial-machine-learned-entity.md#label-text-as-entities-in-example-utterances) with structure and a feature (phrase list) from example utterance on **Intent detail page**
+* [Create and label machine-learning entity](../tutorial-machine-learned-entity.md#label-text-as-entities-in-example-utterances) with structure and a feature (phrase list) from example utterance on **Intent detail page**
 * [Add prebuilt number entity](../tutorial-machine-learned-entity.md#add-prebuilt-number-entity-to-app) from the **Entities list page**.
 * [Add subentity with required feature](../tutorial-machine-learned-entity.md#create-subcomponent-entity-with-constraint-to-help-extract-data) from the **Entity detail page**.
 * [Label example utterance with subentity](../tutorial-machine-learned-entity.md#label-example-utterance-to-teach-luis-about-the-entity) on **Intent detail page**

articles/cognitive-services/LUIS/reference-entity-simple.md

@@ -1,7 +1,7 @@
 ---
 title: Simple entity type - LUIS
 titleSuffix: Azure Cognitive Services
-description: A simple entity describes a single concept from the machine-learned context. Add a phrase list when using a simple entity to improve results.
+description: A simple entity describes a single concept from the machine-learning context. Add a phrase list when using a simple entity to improve results.
 services: cognitive-services
 author: diberry
 manager: nitinme
@@ -13,7 +13,7 @@ ms.author: diberry
 ---
 # Simple entity
 
-A simple entity is a generic entity that describes a single concept and is learned from the machine-learned context. Because simple entities are generally names such as company names, product names, or other categories of names, add a [phrase list](luis-concept-feature.md) when using a simple entity to boost the signal of the names used.
+A simple entity is a generic entity that describes a single concept and is learned from the machine-learning context. Because simple entities are generally names such as company names, product names, or other categories of names, add a [phrase list](luis-concept-feature.md) when using a simple entity to boost the signal of the names used.
 
 **The entity is a good fit when:**
 

articles/cognitive-services/LUIS/reference-pattern-syntax.md

@@ -11,7 +11,7 @@ ms.author: diberry
 Pattern syntax is a template for an utterance. The template should contain words and entities you want to match as well as words and [punctuation](luis-reference-application-settings.md#punctuation-normalization) you want to ignore. It is **not** a regular expression.
 
 > [!CAUTION]
-> Patterns only include machine-learned entity parents, not subentities.
+> Patterns only include machine-learning entity parents, not subentities.
 
 Entities in patterns are surrounded by curly brackets, `{}`. Patterns can include entities, and entities with roles. [Pattern.any](luis-concept-entity-types.md#patternany-entity) is an entity only used in patterns.
 

articles/cognitive-services/LUIS/toc.yml

@@ -378,7 +378,7 @@
     displayName: Calendar, Communication,Email,HomeAutomation,Note,Places, RestaurantReservation,ToDo,Utilities,Weather,Web
   - name: Custom entity reference
     items:
-    - name: Machine-learned entity
+    - name: machine-learning entity
       href: reference-entity-machine-learned-entity.md
     - name: Composite entity
       href: reference-entity-composite.md

articles/cognitive-services/LUIS/tutorial-machine-learned-entity.md

@@ -1,32 +1,32 @@
 ---
-title: "Tutorial: extract structured data with machine-learned entity - LUIS"
-description: Extract structured data from an utterance using the machine-learned entity. To increase the extraction accuracy, add subentities with features.
+title: "Tutorial: extract structured data with machine-learning entity - LUIS"
+description: Extract structured data from an utterance using the machine-learning entity. To increase the extraction accuracy, add subentities with features.
 ms.topic: tutorial
 ms.date: 05/08/2020
 #Customer intent: As a new user, I want to understand how to extract complex data contained in a user utterance.
 ---
 
-# Tutorial: Extract structured data from user utterance with machine-learned entities in Language Understanding (LUIS)
+# Tutorial: Extract structured data from user utterance with machine-learning entities in Language Understanding (LUIS)
 
-In this tutorial, extract structured data from an utterance using the machine-learned entity.
+In this tutorial, extract structured data from an utterance using the machine-learning entity.
 
-The machine-learned entity supports the [model decomposition concept](luis-concept-model.md#v3-authoring-model-decomposition) by providing subentity entities with [features](luis-concept-feature.md).
+The machine-learning entity supports the [model decomposition concept](luis-concept-model.md#v3-authoring-model-decomposition) by providing subentity entities with [features](luis-concept-feature.md).
 
 **In this tutorial, you learn how to:**
 
 > [!div class="checklist"]
 > * Import example app
-> * Add machine-learned entity
+> * Add machine-learning entity
 > * Add subentity and feature
 > * Train, Test, Publish app
 > * Get entity prediction from endpoint
 
 [!INCLUDE [LUIS Free account](includes/quickstart-tutorial-use-free-starter-key.md)]
 
 
-## Why use a machine-learned entity?
+## Why use a machine-learning entity?
 
-This tutorial adds a machine-learned entity to extract data from a user's utterance.
+This tutorial adds a machine-learning entity to extract data from a user's utterance.
 
 The entity defines the data to extract from within the utterance. This includes giving the data a name, a type (if possible), any resolution of the data if there is ambiguity, and the exact text that makes up the data.
 
@@ -38,11 +38,11 @@ In order to define the data, you need to:
 
 Entity decomposability is important for both intent prediction and for data extraction with the entity.
 
-Start with a machine-learned entity, which is the beginning and top-level entity for data extraction. Then decompose the entity into subentities.
+Start with a machine-learning entity, which is the beginning and top-level entity for data extraction. Then decompose the entity into subentities.
 
-While you may not know how detailed you want your entity when you begin your app, a best practice is to start with a machine-learned entity, then decompose with subentities as your app matures.
+While you may not know how detailed you want your entity when you begin your app, a best practice is to start with a machine-learning entity, then decompose with subentities as your app matures.
 
-In this tutorial, you create a machine-learned entity to represent an order for a pizza app. The entity will extract order-related text, pulling out size, and quantity.
+In this tutorial, you create a machine-learning entity to represent an order for a pizza app. The entity will extract order-related text, pulling out size, and quantity.
 
 An utterance of `Please deliver one large cheese pizza to me` should extract `one large cheese pizza` as the order, then also extract `1` for quantity and `large` for size.
 
@@ -56,7 +56,7 @@ Download and save the [app JSON file](https://raw.githubusercontent.com/Azure-Sa
 
 ## Create machine learned entity
 
-To extract details about a pizza order, create a top level, machine-learned `Order` entity.
+To extract details about a pizza order, create a top level, machine-learning `Order` entity.
 
 1. On the **Intents** page, select the **OrderPizza** intent.
 
@@ -180,7 +180,7 @@ To train the app, select **Train**. Training applies the changes, such as the ne
 
 ## Add a new example utterance
 
-1. After training, add a new example utterance to the `OrderPizza` intent to see how well LUIS understands the machine-learned entity.
+1. After training, add a new example utterance to the `OrderPizza` intent to see how well LUIS understands the machine-learning entity.
 
     |Order example utterance|
     |--|
@@ -198,7 +198,7 @@ To train the app, select **Train**. Training applies the changes, such as the ne
     > [!div class="mx-imgBorder"]
     > ![Partial screenshot of new example utterance predicted with entity](media/tutorial-machine-learned-entity/confirm-entity-prediction-for-new-example-utterance-added.png)
 
-    At this point, the machine-learned entity is working because it can find the entity within a new example utterance. As you add example utterances, if the entity is not predicted correctly, label the entity and the subentities. If the entity is predicted correctly, make sure to confirm the predictions.
+    At this point, the machine-learning entity is working because it can find the entity within a new example utterance. As you add example utterances, if the entity is not predicted correctly, label the entity and the subentities. If the entity is predicted correctly, make sure to confirm the predictions.
 
 
 ## Train the app to apply the entity changes to the app
@@ -300,7 +300,7 @@ In order to receive a LUIS prediction in a chat bot or other client application,
 
 ## Next steps
 
-In this tutorial, the app uses a machine-learned entity to find the intent of a user's utterance and extract details from that utterance. Using the machine-learned entity allows you to decompose the details of the entity.
+In this tutorial, the app uses a machine-learning entity to find the intent of a user's utterance and extract details from that utterance. Using the machine-learning entity allows you to decompose the details of the entity.
 
 > [!div class="nextstepaction"]
 > [Add a prebuilt keyphrase entity](luis-quickstart-intent-and-key-phrase.md)

articles/cognitive-services/LUIS/what-is-luis.md

@@ -41,7 +41,7 @@ Your LUIS app contains a domain-specific natural language model. You can start t
 
 * **Prebuilt model** LUIS has many prebuilt domain models including intents, utterances, and prebuilt entities. You can use the prebuilt entities without having to use the intents and utterances of the prebuilt model. [Prebuilt domain models](luis-how-to-use-prebuilt-domains.md) include the entire design for you and are a great way to start using LUIS quickly.
 
-* **Custom model** LUIS gives you several ways to identify your own custom models including intents, and entities. Entities include machine-learned entities, specific or literal entities, and a combination of machine-learned and literal.
+* **Custom model** LUIS gives you several ways to identify your own custom models including intents, and entities. Entities include machine-learning entities, specific or literal entities, and a combination of machine-learning and literal.
 
 ## Build the LUIS model
 Build the model with the [authoring](https://go.microsoft.com/fwlink/?linkid=2092087) APIs or with the [LUIS portal](https://www.luis.ai).

articles/cognitive-services/LUIS/whats-new.md

@@ -16,11 +16,11 @@ Learn what's new in the service. These items include release notes, videos, blog
 * Released as **generally available** (GA):
     * [Language Understanding container](luis-container-howto.md)
     * Preview portal promoted to [current portal](https://www.luis.ai), [previous](https://previous.luis.ai) portal still available
-    * New machine-learned entity creation and labeling experience
-    * [Upgrade process](migrate-from-composite-entity.md) from composite and simple entities to machine-learned entities
+    * New machine-learning entity creation and labeling experience
+    * [Upgrade process](migrate-from-composite-entity.md) from composite and simple entities to machine-learning entities
     * [Setting](how-to-application-settings-portal.md) support for normalizing word variants
 * Preview Authoring API changes
-    * App schema 7.x for nested machine-learned entities
+    * App schema 7.x for nested machine-learning entities
     * [Migration to required feature](luis-migration-authoring-entities.md#api-change-constraint-replaced-with-required-feature)
 * New resources for developers
     * [Continuous integration tools](developer-reference-resource.md#continuous-integration-tools)
@@ -49,13 +49,13 @@ Learn what's new in the service. These items include release notes, videos, blog
     * Italian [prebuilt entities](luis-reference-prebuilt-entities.md#italian-entity-support) - age, currency, dimension, number, and percentage resolution changed from V2.
 * Enhanced user experience in [preview.luis.ai portal](https://preview.luis.ai) - revamped labeling experience to enable building and debugging complex models. Try the preview portal tutorials:
     * [Intents only](tutorial-intents-only.md)
-    * [Decomposable machine-learned entity](tutorial-machine-learned-entity.md)
+    * [Decomposable machine-learning entity](tutorial-machine-learned-entity.md)
 * Advance language understanding capabilities - [building sophisticated language models](luis-concept-entity-types.md) with less effort.
 * Define machine learning features at the model level and enable models to be used as signals to other models, for example using entities as features to intents and to other entities.
 * New, expanded [limits](luis-limits.md) - higher maximum for phrase lists and total phrases, new model as a feature limits
 * Extract information from text in the format of deep hierarchy structure, making conversation applications more powerful.
 
-    ![machine-learned entity image](./media/whats-new/deep-entity-extraction-example.png)
+    ![machine-learning entity image](./media/whats-new/deep-entity-extraction-example.png)
 
 ### September 3, 2019