Merge pull request #200446 from aahill/wow-updates

WoW feedback - #1956756

Author: jborsecnik

articles/cognitive-services/language-service/conversational-language-understanding/how-to/call-api.md

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: how-to
-ms.date: 05/13/2022
+ms.date: 06/03/2022
 ms.author: aahi
 ms.custom: language-service-clu, ignite-fall-2021
 ---
@@ -20,7 +20,7 @@ You can query the deployment programmatically through the [prediction API](https
 
 ## Test deployed model
 
-You can use the Language Studio to submit an utterance, get predictions and visualize the results.
+You can use Language Studio to submit an utterance, get predictions and visualize the results.
 
 [!INCLUDE [Test model](../includes/language-studio/test-model.md)]
 

articles/cognitive-services/language-service/conversational-language-understanding/how-to/create-project.md

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: how-to
-ms.date: 05/13/2022
+ms.date: 06/03/2022
 ms.author: aahi
 ms.custom: language-service-clu, ignite-fall-2021
 ---
@@ -34,7 +34,7 @@ Before you start using CLU, you will need an Azure Language resource.
 
 [!INCLUDE [create a new resource from the Azure portal](../includes/resource-creation-azure-portal.md)]
 
-[!INCLUDE [create a new resource from the Language Studio](../includes/resource-creation-language-studio.md)]
+[!INCLUDE [create a new resource from Language Studio](../includes/resource-creation-language-studio.md)]
 
 
 ## Sign in to Language Studio

articles/cognitive-services/language-service/conversational-language-understanding/how-to/tag-utterances.md

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: how-to
-ms.date: 04/26/2022
+ms.date: 06/03/2022
 ms.author: aahi
 ms.custom: language-service-clu, ignite-fall-2021
 ---
@@ -87,7 +87,7 @@ Use the following steps to label your utterances:
     * *Unique utterances per labeled entity* where each utterance is counted if it contains at least one labeled instance of this entity.
     * *Utterances per intent* where you can view count of utterances per intent.
 
-:::image type="content" source="../media/label-distribution.png" alt-text="A screenshot showing entity distribution in the Language Studio." lightbox="../media/label-distribution.png":::
+:::image type="content" source="../media/label-distribution.png" alt-text="A screenshot showing entity distribution in Language Studio." lightbox="../media/label-distribution.png":::
 
 
   > [!NOTE]

articles/cognitive-services/language-service/conversational-language-understanding/includes/language-studio/test-model.md

@@ -5,14 +5,14 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: include
-ms.date: 05/12/2022
+ms.date: 06/03/2022
 ms.author: aahi
 ---
 
 
 ## Test the model
 
-To test your model from the Language studio
+To test your model from Language studio
 
 1. Select **Test model** from the left side menu.
 

articles/cognitive-services/language-service/conversational-language-understanding/includes/quickstarts/language-studio.md

@@ -5,7 +5,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: include
-ms.date: 05/16/2022
+ms.date: 06/03/2022
 ms.author: aahi
 ms.custom: ignite-fall-2021
 ---
@@ -38,7 +38,7 @@ To train a model, you need to start a training job. The output of a successful t
 
 ## Deploy your model
 
-Generally after training a model you would review its evaluation details. In this quickstart, you will just deploy your model, and make it available for you to try in the Language studio, or you can call the [prediction API](https://aka.ms/clu-apis).
+Generally after training a model you would review its evaluation details. In this quickstart, you will just deploy your model, and make it available for you to try in Language studio, or you can call the [prediction API](https://aka.ms/clu-apis).
 
 [!INCLUDE [Deploy model](../language-studio/deploy-model.md)]
 

articles/cognitive-services/language-service/custom-named-entity-recognition/concepts/data-formats.md

@@ -8,14 +8,14 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: conceptual
-ms.date: 05/24/2022
+ms.date: 06/03/2022
 ms.author: aahi
 ms.custom: language-service-custom-ner, ignite-fall-2021, event-tier1-build-2022
 ---
 
 # Accepted custom NER data formats
 
-If you are trying to [import your data](../how-to/create-project.md#import-project) into custom NER, it has to follow a specific format. If you don't have data to import, you can [create your project](../how-to/create-project.md) and use the Language Studio to [label your documents](../how-to/tag-data.md).
+If you are trying to [import your data](../how-to/create-project.md#import-project) into custom NER, it has to follow a specific format. If you don't have data to import, you can [create your project](../how-to/create-project.md) and use Language Studio to [label your documents](../how-to/tag-data.md).
 
 ## Labels file format
 

articles/cognitive-services/language-service/custom-named-entity-recognition/how-to/call-api.md

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: how-to
-ms.date: 05/24/2022
+ms.date: 06/03/2022
 ms.author: aahi
 ms.devlang: csharp, python
 ms.custom: language-service-custom-ner, event-tier1-build-2022
@@ -21,7 +21,7 @@ You can query the deployment programmatically using the [Prediction API](https:/
 
 ## Test deployed model
 
-You can use the Language Studio to submit the custom entity recognition task and visualize the results. 
+You can use Language Studio to submit the custom entity recognition task and visualize the results. 
 
 [!INCLUDE [Test model](../includes/language-studio/test-model.md)]
 

articles/cognitive-services/language-service/custom-named-entity-recognition/how-to/create-project.md

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: how-to
-ms.date: 05/24/2022
+ms.date: 06/03/2022
 ms.author: aahi
 ms.custom: language-service-custom-ner, references_regions, ignite-fall-2021, event-tier1-build-2022
 ---
@@ -46,7 +46,7 @@ You can create a resource in the following ways:
 
 [!INCLUDE [create a new resource from the Azure portal](../includes/resource-creation-azure-portal.md)]
 
-[!INCLUDE [create a new resource from the Language Studio](../includes/language-studio/resource-creation-language-studio.md)]
+[!INCLUDE [create a new resource from Language Studio](../includes/language-studio/resource-creation-language-studio.md)]
 
 [!INCLUDE [create a new resource with Azure PowerShell](../includes/resource-creation-powershell.md)]
 
@@ -103,7 +103,7 @@ If you have already labeled data, you can use it to get started with the service
 
 ### [Language Studio](#tab/language-studio)
 
-[!INCLUDE [Delete project using the Language studio](../includes/language-studio/delete-project.md)]
+[!INCLUDE [Delete project using Language studio](../includes/language-studio/delete-project.md)]
 
 ### [Rest APIs](#tab/rest-api)
 

articles/cognitive-services/language-service/custom-named-entity-recognition/includes/language-studio/create-project.md

@@ -6,13 +6,13 @@ ms.service: cognitive-services
 ms.subservice: language-service
 ms.custom: event-tier1-build-2022
 ms.topic: include
-ms.date: 05/05/2022
+ms.date: 06/03/2022
 ms.author: aahi
 ---
 
 1. Sign into the [Language Studio](https://aka.ms/languageStudio). A window will appear to let you select your subscription and Language resource. Select the Language resource you created in the above step. 
 
-2. Under the **Entity extraction** section of Language Studio, select **Custom named entity recognition**.
+2. Under the **Extract information** section of Language Studio, select **Custom named entity recognition**.
 
     :::image type="content" source="../../media/select-custom-ner.png" alt-text="A screenshot showing the location of custom NER in the Language Studio landing page." lightbox="../../media/select-custom-ner.png":::
 

articles/cognitive-services/language-service/custom-named-entity-recognition/includes/quickstarts/language-studio.md

@@ -19,7 +19,7 @@ ms.author: aahi
 Before you can use custom NER, you’ll need to create an Azure Language resource, which will give you the credentials that you need to create a project and start training a model. You’ll also need an Azure storage account, where you can upload your dataset that will be used in building your model.
 
 > [!IMPORTANT]
-> To get started quickly, we recommend creating a new Azure Language resource using the steps provided in this article, which will let you create the Language, and create and/or connect a storage account at the same time, which is easier than doing it later.
+> To get started quickly, we recommend creating a new Azure Language resource using the steps provided in this article, which will let you create the Language resource, and create and/or connect a storage account at the same time, which is easier than doing it later.
 >
 > If you have a pre-existing resource that you'd like to use, you will need to connect it to storage account. See [guidance to using a pre-existing resource](../../how-to/create-project.md#using-a-pre-existing-language-resource) for information.
 
@@ -43,7 +43,7 @@ Typically after you create a project, you go ahead and start [tagging the docume
 
 ## Deploy your model
 
-Generally after training a model you would review it's [evaluation details](../../how-to/view-model-evaluation.md) and [make improvements](../../how-to/improve-model.md) if necessary. In this quickstart, you will just deploy your model, and make it available for you to try in the Language studio, or you can call the [prediction API](https://aka.ms/ct-runtime-swagger).
+Generally after training a model you would review it's [evaluation details](../../how-to/view-model-evaluation.md) and [make improvements](../../how-to/improve-model.md) if necessary. In this quickstart, you will just deploy your model, and make it available for you to try in Language studio, or you can call the [prediction API](https://aka.ms/ct-runtime-swagger).
 
 [!INCLUDE [Deploy a model using Language Studio](../language-studio/deploy-model.md)]