edits

Author: v-geberr

articles/cognitive-services/LUIS/luis-concept-entity-types.md

@@ -57,19 +57,19 @@ All intents, including the **None** intent, should have entities labeled. This h
 ## Types of entities
 LUIS offers many types of entities; prebuilt entities, custom machine learned entities and list entities.
 
-| Name |Type | Can label | Description |
-| -- |--|--|--|
-| **Prebuilt** |[Custom](#prebuilt)| |  **Definition**<br>Built-in types that represent common concepts. <br><br>**Example**<br>:  number, ordinal, temperature, dimension, money, age, percentage, email, URL, phone number, and key phrase. <br><br>Prebuilt entity names are reserved. <br><br>All prebuilt entities that are added to the application are returned in the [endpoint](luis-glossary.md#endpoint) query. For more information, see [Prebuilt entities](./Pre-builtEntities.md). <!-- TBD: is this where the Sentiment Analysis Key phrase extraction entity concept should be? --><br/><br/>[Example response for entity](luis-concept-data-extraction.md#prebuilt-entity-data)|
-|<!-- added week of 3/21/08 --> **Regular Expression** | [RegEx](#regex)||**Definition**<br>Custom regular expression for formatted text. It ignores case and ignores cultural variant.  <br><br>This entity is good for words or phrases that are consistently formatted with any variation that is also consistent.<br><br>Regular expression matching is applied after spell-check alterations. <br><br>If the regular expression is too complex, such as using many brackets, you are not able to add the expression to the model. <br><br>**Example**<br>`kb[0-9]{6,}` matches kb123456.<br/><br/>[Quickstart](luis-quickstart-intents-regex-entity.md)<br>[Example response for entity](luis-concept-data-extraction.md)|
-| **Simple** | [Machine-learned](#machine-learned) | ✔ | **Definition**<br>A simple entity is a generic entity that describes a single concept and is learned from machine-learned context. Context include word choice, word placement, and utterance length.<br/><br/>This is a good entity for words or phrases that are not consistently formatted but indicate the same thing. <br/><br/>[Quickstart](luis-quickstart-primary-and-secondary-data.md)<br>[Example response for entity](luis-concept-data-extraction.md#simple-entity-data)|  
-| **List** | [Exact match](#exact-match)|| **Definition**<br>List entities represent a fixed, closed set of related words in your system. <br><br>Each list entity may have one or more forms. Best used for a known set of variations on ways to represent the same concept.<br/><br/>**Example**<br>Drink list:<br>
-tea (synonyms: earl grey, blank, herbal, chai)<br>
-coffee (synonyms: latte, mocha, espresso)<br>
-soft drink: (synonyms: orange, cola, ginger)<br>
+| Name | Can label | Description |
+| -- |--|--|
+| **Prebuilt** <br/>[Custom](#prebuilt)| |  **Definition**<br>Built-in types that represent common concepts. <br><br>**List**<br/>key phrase number, ordinal, temperature, dimension, money, age, percentage, email, URL, phone number, and key phrase. <br><br>Prebuilt entity names are reserved. <br><br>All prebuilt entities that are added to the application are returned in the [endpoint](luis-glossary.md#endpoint) query. For more information, see [Prebuilt entities](./Pre-builtEntities.md). <br/><br/>[Example response for entity](luis-concept-data-extraction.md#prebuilt-entity-data)|
+|<!-- added week of 3/21/08 --> **Regular Expression**<br/>[RegEx](#regex)||**Definition**<br>Custom regular expression for formatted text. It ignores case and ignores cultural variant.  <br><br>This entity is good for words or phrases that are consistently formatted with any variation that is also consistent.<br><br>Regular expression matching is applied after spell-check alterations. <br><br>If the regular expression is too complex, such as using many brackets, you are not able to add the expression to the model. <br><br>**Example**<br>`kb[0-9]{6,}` matches kb123456.<br/><br/>[Quickstart](luis-quickstart-intents-regex-entity.md)<br>[Example response for entity](luis-concept-data-extraction.md)|
+| **Simple** <br/>[Machine-learned](#machine-learned) | ✔ | **Definition**<br>A simple entity is a generic entity that describes a single concept and is learned from machine-learned context. Context include word choice, word placement, and utterance length.<br/><br/>This is a good entity for words or phrases that are not consistently formatted but indicate the same thing. <br/><br/>[Quickstart](luis-quickstart-primary-and-secondary-data.md)<br/>[Example response for entity](luis-concept-data-extraction.md#simple-entity-data)|  
+| **List** <br/>[Exact match](#exact-match)|| **Definition**<br>List entities represent a fixed, closed set of related words in your system. <br><br>Each list entity may have one or more forms. Best used for a known set of variations on ways to represent the same concept.<br/><br/>**Example**<br/>Drink list:<br/>
+tea (synonyms: earl grey, blank, herbal, chai)<br/>
+coffee (synonyms: latte, mocha, espresso)<br/>
+soft drink: (synonyms: orange, cola, ginger)<br/>
  <br/><br/> LUIS does not discover additional values for list entities. Use the to see [semantic dictionary](luis-glossary.md#semantic-dictionary) to find suggestions for new words based on the current list.<br/><br>If there is more than one list entity with the same value, each entity is returned in the endpoint query. <br/><br/>[Quickstart](luis-quickstart-intent-and-list-entity.md)<br>[Example response for entity](luis-concept-data-extraction.md#list-entity-data)| 
-| **Pattern.any** | [Mixed](#mixed) | ✔|**Definition**<br>Patterns.any is a variable-length placeholder used only in a pattern's template utterance to mark where the entity begins and ends.  <br><br>**Example**<br>Given an utterance search for books based on title, the pattern.any extracts the complete title. A template utterance using pattern.any is `Who wrote {BookTitle}[?]`.<br/><br/>[Tutorial](luis-tutorial-pattern.md)<br>[Example response for entity](luis-concept-data-extraction.md#composite-entity-data)|  
-| **Composite** | [Machine-learned](#machine-learned) | ✔|**Definition**<br>A composite entity is made up of other entities, such as prebuilt entities, and simple. The separate entities form a whole entity. List entities are not allowed in composite entities. <br><br>**Example**<br>A composite entity named PlaneTicketOrder may have child entities prebuilt `number` and `ToLocation`. <br/><br/>[Tutorial](luis-tutorial-composite-entity.md)<br>[Example response for entity](luis-concept-data-extraction.md#composite-entity-data)|  
-| **Hierarchical** | [Machine-learned](#machine-learned) |✔ | **Definition**<br>A hierarchical entity is a category of contextually learned entities.<br><br>**Example**<br>Given a hierarchical entity of `Location` with children `ToLocation` and `FromLocation`, each child can be determined based on the **context** within the utterance. In the utterance, `Book 2 tickets from Seattle to New York`, the `ToLocation` and `FromLocation` are contextually different based the words around them. <br/><br/>**Do not use if**<br>If you are looking for an entity that has exact text matches for children regardless of context, you should use a List entity. If you are looking for a parent-child relationship with other entity types, you should use the Composite entity.<br/><br/>[Quickstart](luis-quickstart-intent-and-hier-entity.md)<br>[Example response for entity](luis-concept-data-extraction.md#hierarchical-entity-data)|
+| **Pattern.any** <br/>[Mixed](#mixed) | ✔|**Definition**<br>Patterns.any is a variable-length placeholder used only in a pattern's template utterance to mark where the entity begins and ends.  <br><br>**Example**<br>Given an utterance search for books based on title, the pattern.any extracts the complete title. A template utterance using pattern.any is `Who wrote {BookTitle}[?]`.<br/><br/>[Tutorial](luis-tutorial-pattern.md)<br>[Example response for entity](luis-concept-data-extraction.md#composite-entity-data)|  
+| **Composite** <br/>[Machine-learned](#machine-learned) | ✔|**Definition**<br>A composite entity is made up of other entities, such as prebuilt entities, and simple. The separate entities form a whole entity. List entities are not allowed in composite entities. <br><br>**Example**<br>A composite entity named PlaneTicketOrder may have child entities prebuilt `number` and `ToLocation`. <br/><br/>[Tutorial](luis-tutorial-composite-entity.md)<br>[Example response for entity](luis-concept-data-extraction.md#composite-entity-data)|  
+| **Hierarchical** <br/>[Machine-learned](#machine-learned) |✔ | **Definition**<br>A hierarchical entity is a category of contextually learned entities.<br><br>**Example**<br>Given a hierarchical entity of `Location` with children `ToLocation` and `FromLocation`, each child can be determined based on the **context** within the utterance. In the utterance, `Book 2 tickets from Seattle to New York`, the `ToLocation` and `FromLocation` are contextually different based the words around them. <br/><br/>**Do not use if**<br>If you are looking for an entity that has exact text matches for children regardless of context, you should use a List entity. If you are looking for a parent-child relationship with other entity types, you should use the Composite entity.<br/><br/>[Quickstart](luis-quickstart-intent-and-hier-entity.md)<br>[Example response for entity](luis-concept-data-extraction.md#hierarchical-entity-data)|
 
 <a name="prebuilt"></a>
 **Prebuilt** entities are custom entities provided by LUIS. Some of these entities are defined in the open-source [Recognizers-Text](https://github.com/Microsoft/Recognizers-Text) project. There are many [examples](https://github.com/Microsoft/Recognizers-Text/tree/master/Specs) in the /Specs directory for the supported cultures. If your specific culture or entity isn't currently supported, contribute to the project.