[Cogsvcs] LUIS machine-learned -> learning 3

Author: diberry

articles/cognitive-services/LUIS/luis-concept-patterns.md

@@ -30,16 +30,16 @@ If an app has between 10 and 20 utterances with different lengths of sentence, d
 Patterns solve the following situations:
 
 * The intent score is low
-* The correct intent is not the top score but too close to the top score. 
+* The correct intent is not the top score but too close to the top score.
 
 ## Patterns are not a guarantee of intent
 Patterns use a mix of prediction technologies. Setting an intent for a template utterance in a pattern is not a guarantee of the intent prediction but it is a strong signal.
 
 <a name="patterns-do-not-improve-entity-detection"/></a>
 
-## Patterns do not improve machine-learned entity detection
+## Patterns do not improve machine-learning entity detection
 
-A pattern is primarily meant to help the prediction of intents and roles. The _pattern.any_ entity is used to extract free-form entities. While patterns use entities, a pattern does not help detect a machine-learned entity.
+A pattern is primarily meant to help the prediction of intents and roles. The _pattern.any_ entity is used to extract free-form entities. While patterns use entities, a pattern does not help detect a machine-learning entity.
 
 Do not expect to see improved entity prediction if you collapse multiple utterances into a single pattern. For simple entities to fire, you need to add utterances or use list entities else your pattern will not fire.
 
@@ -53,7 +53,7 @@ Given enough example utterances, LUIS would be able to increase prediction confi
 A pattern is matched based on detecting the entities inside the pattern first, then validating the rest of the words and word order of the pattern. Entities are required in the pattern for a pattern to match. The pattern is applied at the token level, not the character level.
 
 ## Pattern-only apps
-You can build an app with intents that have no example utterances, as long as there's a pattern for each intent. For a pattern-only app, the pattern shouldn't contain machine-learned entities because these do require example utterances.
+You can build an app with intents that have no example utterances, as long as there's a pattern for each intent. For a pattern-only app, the pattern shouldn't contain machine-learning entities because these do require example utterances.
 
 ## Pattern.any entity
 

articles/cognitive-services/LUIS/luis-concept-utterance.md

@@ -6,7 +6,7 @@ ms.date: 05/19/2020
 ---
 # Understand what good utterances are for your LUIS app
 
-**Utterances** are input from the user that your app needs to interpret. To train LUIS to extract intents and entities from them, it's important to capture a variety of different example utterances for each intent. Active learning, or the process of continuing to train on new utterances, is essential to machine-learned intelligence that LUIS provides.
+**Utterances** are input from the user that your app needs to interpret. To train LUIS to extract intents and entities from them, it's important to capture a variety of different example utterances for each intent. Active learning, or the process of continuing to train on new utterances, is essential to machine-learning intelligence that LUIS provides.
 
 Collect utterances that you think users will enter. Include utterances, which mean the same thing but are constructed in a variety of different ways:
 

articles/cognitive-services/LUIS/luis-glossary.md

@@ -134,9 +134,9 @@ The entity schema is the structure you define for machine learned entities with
 
 ### Entity's subentity
 
-A subentity is a child entity of a machine-learned entity.
+A subentity is a child entity of a machine-learning entity.
 
-### Non-machine-learned entity
+### Non-machine-learning entity
 
 An entity that uses text matching to extract data:
 * List entity
@@ -159,7 +159,7 @@ See Prebuilt model's entry for [prebuilt entity](#prebuilt-entity)
 
 In machine learning, a feature is a characteristic that helps the model recognize a particular concept. It is a hint that LUIS can use, but not a hard rule.
 
-This term is also referred to as a **[machine-learned feature](luis-concept-feature.md)**.
+This term is also referred to as a **[machine-learning feature](luis-concept-feature.md)**.
 
 These hints are used in conjunction with the labels to learn how to predict new data. LUIS supports both phrase lists and using other models as features.
 

articles/cognitive-services/LUIS/luis-how-to-add-entities.md

@@ -20,28 +20,28 @@ The entity represents a word or phrase inside the utterance that you want extrac
 
 ## Plan entities, then create and label
 
-Machine-learned entities can be created from the example utterances or created from the **Entities** page.
+machine-learning entities can be created from the example utterances or created from the **Entities** page.
 
-In general, a best practice is to spend time planning the entities before creating a machine-learned entity in the portal. Then create the machine-learned entity from the example utterance with as much detail in the subentities and features you know at the time. The [decomposable entity tutorial](tutorial-machine-learned-entity.md) demonstrates how to use this method.
+In general, a best practice is to spend time planning the entities before creating a machine-learning entity in the portal. Then create the machine-learning entity from the example utterance with as much detail in the subentities and features you know at the time. The [decomposable entity tutorial](tutorial-machine-learned-entity.md) demonstrates how to use this method.
 
 As part of planning the entities, you may know you need text-matching entities (such as prebuilt entities, regular expression entities, or list entities). You can create these from the **Entities** page before they are labeled in example utterances.
 
-When labeling, you can either label individual entities then build up to a parent machine-learned entity. Or you can start with a parent machine-learned entity and decompose into child entities.
+When labeling, you can either label individual entities then build up to a parent machine-learning entity. Or you can start with a parent machine-learning entity and decompose into child entities.
 
 > [!TIP]
 >Label all words that may indicate an entity, even if the words are not used when extracted in the client application.
 
 ## When to create an entity
 
-After planning your entities, you should create your machine-learned entities and subentities. This may require adding prebuilt entities or text-matching entities to provide features for your machine-learned entities. These should all be done before labeling.
+After planning your entities, you should create your machine-learning entities and subentities. This may require adding prebuilt entities or text-matching entities to provide features for your machine-learning entities. These should all be done before labeling.
 
 Once you begin labeling example utterances, you can create machine learned entities or extend list entities.
 
 Use the following table to understand where to create or add each entity type to the app.
 
 |Entity type|Where to create entity in the LUIS portal|
 |--|--|
-|Machine-learned entity|Entities or Intent detail|
+|machine-learning entity|Entities or Intent detail|
 |List entity|Entities or Intent detail|
 |Regular expression entity|Entities|
 |Pattern.any entity|Entities|
@@ -166,7 +166,7 @@ In this procedure, add `origin` and `destination` roles to a prebuilt geographyV
 ### Label text with a role in an example utterance
 
 > [!TIP]
-> Roles can be replaced by labeling with subentities of a machine-learned entities.
+> Roles can be replaced by labeling with subentities of a machine-learning entities.
 
 1. Sign in to the [LUIS portal](https://www.luis.ai), and select your **Subscription** and **Authoring resource** to see the apps assigned to that authoring resource.
 1. Open your app by selecting its name on **My Apps** page.

articles/cognitive-services/LUIS/luis-how-to-model-intent-pattern.md

@@ -17,7 +17,7 @@ ms.author: diberry
 After a LUIS app receives endpoint utterances, use a [pattern](luis-concept-patterns.md) to improve prediction accuracy for utterances that reveal a pattern in word order and word choice. Patterns use specific [syntax](luis-concept-patterns.md#pattern-syntax) to indicate the location of: [entities](luis-concept-entity-types.md), entity [roles](luis-concept-roles.md), and optional text.
 
 > [!CAUTION]
-> Patterns only include machine-learned entity parents, not subentities.
+> Patterns only include machine-learning entity parents, not subentities.
 
 ## Add template utterance using correct syntax
 
@@ -52,7 +52,7 @@ After a LUIS app receives endpoint utterances, use a [pattern](luis-concept-patt
 
 1. In the **Choose an entity type** dialog box, enter the entity name in the **Name** box, and select **Pattern.Any** as the **Type** then select **Create**.
 
-    Once you [create a pattern utterance](luis-how-to-model-intent-pattern.md) using this entity, the entity is extracted with a combined machine-learned and text-matching algorithm.
+    Once you [create a pattern utterance](luis-how-to-model-intent-pattern.md) using this entity, the entity is extracted with a combined machine-learning and text-matching algorithm.
 
 ## Adding example utterances as pattern
 
@@ -68,7 +68,7 @@ If you want to add a pattern for an entity, the _easiest_ way is to create the p
 
     The utterance must include an entity in order to create a pattern from the utterance.
 
-1. In the pop-up box, select **Done** on the **Confirm patterns** page. You don't need to define the entities' subentities, or features. You only need to list the machine-learned entity.
+1. In the pop-up box, select **Done** on the **Confirm patterns** page. You don't need to define the entities' subentities, or features. You only need to list the machine-learning entity.
 
     > [!div class="mx-imgBorder"]
     > ![Screenshot of confirming example utterance as a template pattern on the Intent details page.](./media/luis-how-to-model-intent-pattern/confirm-patterns-from-example-utterance-intent-detail-page.png)

articles/cognitive-services/LUIS/luis-limits.md

@@ -22,7 +22,7 @@ If your app exceeds the LUIS model limits, consider using a [LUIS dispatch](luis
 | External entities | no limits |
 | [Intents][intents]|500 per application: 499 custom intents, and the required _None_ intent.<br>[Dispatch-based](https://aka.ms/dispatch-tool) application has corresponding 500 dispatch sources.|
 | [List entities](./luis-concept-entity-types.md) | Parent: 50, child: 20,000 items. Canonical name is *default character max. Synonym values have no length restriction. |
-| [Machine-learned entities + roles](./luis-concept-entity-types.md):<br> composite,<br>simple,<br>entity role|A limit of either 100 parent entities or 330 entities, whichever limit the user hits first. A role counts as an entity for the purpose of this limit. An example is a composite with a simple entity, which has 2 roles is: 1 composite + 1 simple + 2 roles = 4 of the 330 entities.<br>Subentities can be nested up to 5 levels.|
+| [machine-learning entities + roles](./luis-concept-entity-types.md):<br> composite,<br>simple,<br>entity role|A limit of either 100 parent entities or 330 entities, whichever limit the user hits first. A role counts as an entity for the purpose of this limit. An example is a composite with a simple entity, which has 2 roles is: 1 composite + 1 simple + 2 roles = 4 of the 330 entities.<br>Subentities can be nested up to 5 levels.|
 |Model as a feature| Maximum number of models that can be used as a feature to a specific model to be 10 models. The maximum number of phrase lists used as a feature for a specific model to be 10 phrase lists.|
 | [Preview - Dynamic list entities](https://aka.ms/luis-api-v3-doc#dynamic-lists-passed-in-at-prediction-time)|2 lists of ~1k per query prediction endpoint request|
 | [Patterns](luis-concept-patterns.md)|500 patterns per application.<br>Maximum length of pattern is 400 characters.<br>3 Pattern.any entities per pattern<br>Maximum of 2 nested optional texts in pattern|
@@ -47,7 +47,7 @@ Object names must be unique when compared to other objects of the same level.
 |Objects|Restrictions|
 |--|--|
 |Intent, entity|All intent and entity names must be unique in a version of an app.|
-|ML entity components|All machine-learned entity components (child entities) must be unique, within that entity for components at the same level.|
+|ML entity components|All machine-learning entity components (child entities) must be unique, within that entity for components at the same level.|
 |Features | All named features, such as phrase lists, must be unique within a version of an app.|
 |Entity roles|All roles on an entity or entity component must be unique when they are at the same entity level (parent, child, grandchild, etc.).|