Merge pull request #41909 from v-geberr/0522-concept-2

0522 concept 2

Author: PRMerger20

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

@@ -7,16 +7,17 @@ manager: kaiqb
 ms.service: cognitive-services
 ms.component: language-understanding
 ms.topic: article
-ms.date: 05/07/2018
+ms.date: 05/22/2018
 ms.author: v-geberr
 ---
 # Entities in LUIS
 
-Entities are key data in your application’s domain.<!-- An entity represents a class including a collection of similar objects (places, things, people, events or concepts). Entities describe information relevant to the intent, and sometimes they are essential for your app to perform its task. For example, a News Search app may include entities such as “topic”, “source”, “keyword” and “publishing date”, which are key data to search for news. In a travel booking app, the “location”, “date”, "airline", "travel class" and "tickets" are key information for flight booking (relevant to the "Bookflight" intent). 
---> 
+Entities are words or phrases in utterances that are key data in your application’s domain.
 
 ## Entity compared to intent
-The entity represents the prediction for a word or phrase inside the utterance that you want extracted. By comparison, the intent represents the prediction of the entire utterance. 
+The entity represents a word or phrase inside the utterance that you want extracted. An utterance can include many entities or none at all. An entity represents a class including a collection of similar objects (places, things, people, events or concepts). Entities describe information relevant to the intent, and sometimes they are essential for your app to perform its task. For example, a News Search app may include entities such as “topic”, “source”, “keyword” and “publishing date”, which are key data to search for news. In a travel booking app, the “location”, “date”, "airline", "travel class" and "tickets" are key information for flight booking (relevant to the "Bookflight" intent).
+
+By comparison, the intent represents the prediction of the entire utterance. 
 
 ## Entities represent data
 Entities are data you want to pull from the utterance. This can be a name, date, product name, or any group of words. 
@@ -56,15 +57,15 @@ 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 like numbers, dates, and email. <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 that ignores case and ignores cultural variant.  <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.<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/> 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)|
+| 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 along with their synoymns 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/>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** <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. 
@@ -81,7 +82,6 @@ LUIS offers many types of entities; prebuilt entities, custom machine learned en
 <a name="mixed"></a>
 **Mixed** entities use a combination of entity detection methods.
 
-
 ## Entity limits
 Review [limits](luis-boundaries.md#model-boundaries) to understand how many of each type of entity you can add to a model.