Merge pull request #1625 from MicrosoftDocs/main

11/19/2024 PM Publish

Author: Taojunshen

articles/ai-services/content-moderator/text-moderation-api.md

@@ -1,27 +1,29 @@
 ---
 title: Text Moderation - Content Moderator
 titleSuffix: Azure AI services
-description: Use text moderation for possible unwanted text, personal data, and custom lists of terms.
+description: Use text moderation to detect potentially unwanted text, personal data, and custom lists of terms.
 #services: cognitive-services
 author: PatrickFarley
 manager: nitinme
 ms.service: azure-ai-content-moderator
 ms.topic: conceptual
-ms.date: 01/18/2024
+ms.date: 11/06/2024
 ms.author: pafarley
 
 ---
 
-# Learn text moderation concepts
+# Text moderation concepts
 
-Use Content Moderator's text moderation models to analyze text content, such as chat rooms, discussion boards, chatbots, e-commerce catalogs, and documents.
+[!INCLUDE [deprecation notice](includes/tool-deprecation.md)]
+
+You can use Azure Content Moderator's text moderation models to analyze text content, such as chat rooms, discussion boards, chatbots, e-commerce catalogs, and documents.
 
 The service response includes the following information:
 
 - Profanity: term-based matching with built-in list of profane terms in various languages
 - Classification: machine-assisted classification into three categories
 - Personal data
-- Auto-corrected text
+- Autocorrected text
 - Original text
 - Language
 
@@ -40,13 +42,13 @@ If the API detects any profane terms in any of the [supported languages](./langu
 ```
 
 > [!NOTE]
-> For the **language** parameter, assign `eng` or leave it empty to see the machine-assisted **classification** response (preview feature). **This feature supports English only**.
+> For the `language` parameter, assign `eng` or leave it empty to see the machine-assisted *classification* response (preview feature). **This feature supports English only**.
 >
-> For **profanity terms** detection, use the [ISO 639-3 code](http://www-01.sil.org/iso639-3/codes.asp) of the supported languages listed in this article, or leave it empty.
+> For *profanity terms* detection, use the [ISO 639-3 code](http://www-01.sil.org/iso639-3/codes.asp) of the supported languages listed in this article, or leave it empty.
 
 ## Classification
 
-Content Moderator's machine-assisted **text classification feature** supports **English only**, and helps detect potentially undesired content. The flagged content may be assessed as inappropriate depending on context. It conveys the likelihood of each category. The feature uses a trained model to identify possible abusive, derogatory or discriminatory language. This includes slang, abbreviated words, offensive, and intentionally misspelled words.
+Content Moderator's machine-assisted *text classification feature* supports *English only*, and helps detect potentially undesired content. The flagged content might be assessed as inappropriate depending on context. It conveys the likelihood of each category. The feature uses a trained model to identify possible abusive, derogatory, or discriminatory language. This includes slang, abbreviated words, offensive, and intentionally misspelled words.
 
 The following extract in the JSON extract shows an example output:
 
@@ -67,10 +69,10 @@ The following extract in the JSON extract shows an example output:
 
 ### Explanation
 
-- `Category1` refers to potential presence of language that may be considered sexually explicit or adult in certain situations.
-- `Category2` refers to potential presence of language that may be considered sexually suggestive or mature in certain situations.
-- `Category3` refers to potential presence of language that may be considered offensive in certain situations.
-- `Score` is between 0 and 1. The higher the score, the higher the model is predicting that the category may be applicable. This feature relies on a statistical model rather than manually coded outcomes. We recommend testing with your own content to determine how each category aligns to your requirements.
+- `Category1` refers to the potential presence of language that might be considered sexually explicit or adult in certain situations.
+- `Category2` refers to the potential presence of language that might be considered sexually suggestive or mature in certain situations.
+- `Category3` refers to the potential presence of language that might be considered offensive in certain situations.
+- `Score` is between 0 and 1. The higher the score, the higher the probability that the category might be applicable. This feature relies on a statistical model rather than manually coded outcomes. We recommend testing with your own content to determine how each category aligns to your requirements.
 - `ReviewRecommended` is either true or false depending on the internal score thresholds. Customers should assess whether to use this value or decide on custom thresholds based on their content policies.
 
 ## Personal data
@@ -120,24 +122,24 @@ The following example shows a sample response:
 }
 ```
 
-## Auto-correction
+## Autocorrection
 
-The text moderation response can optionally return the text with basic auto-correction applied. 
+The text moderation response can optionally return the text with basic autocorrection applied. 
 
 For example, the following input text has a misspelling.
 
 > The quick brown fox jumps over the lazzy dog.
 
-If you specify auto-correction, the response contains the corrected version of the text:
+If you specify autocorrection, the response contains the corrected version of the text:
 
 > The quick brown fox jumps over the lazy dog.
 
-## Creating and managing your custom lists of terms
+## Create and manage your custom lists of terms
 
-While the default, global list of terms works great for most cases, you may want to screen against terms that are specific to your business needs. For example, you may want to filter out any competitive brand names from posts by users.
+While the default, global list of terms works great for most cases, you might want to screen against terms that are specific to your business needs. For example, you might want to filter out any competitive brand names from posts by users.
 
 > [!NOTE]
-> There is a maximum limit of **5 term lists** with each list to **not exceed 10,000 terms**.
+> There is a maximum limit of *five term lists* with each list to *not exceed 10,000 terms*.
 >
 
 The following example shows the matching List ID:
@@ -152,8 +154,8 @@ The following example shows the matching List ID:
     }
 ```
 
-The Content Moderator provides a [Term List API](/rest/api/cognitiveservices/contentmoderator/list-management-term-lists) with operations for managing custom term lists. Check out the [Term Lists .NET quickstart](term-lists-quickstart-dotnet.md) if you are familiar with Visual Studio and C#.
+The Content Moderator provides a [Term List API](/rest/api/cognitiveservices/contentmoderator/list-management-term-lists) with operations for managing custom term lists. Check out the [Term Lists .NET quickstart](term-lists-quickstart-dotnet.md) if you're familiar with Visual Studio and C#.
 
-## Next steps
+## Related content
 
-Test out the APIs with the [Quickstart](client-libraries.md).
+- [Quickstart: Use the Content Moderator client library](client-libraries.md)

articles/ai-services/content-understanding/audio/overview.md

@@ -63,9 +63,9 @@ Content Understanding offers advanced audio capabilities, including:
 
 Content Understanding offers customizable audio analyzer templates:
 
-* **Post-call analytics**. Analyze call recordings to generate conversation transcripts, call summaries, sentiment assessments, and more.
+* **Post-call analysis**. Analyze call recordings to generate conversation transcripts, call summaries, sentiment assessments, and more.
 
-* **Conversation summarization**. Generate transcriptions, summaries, and sentiment assessments from conversation audio recordings.
+* **Conversation analysis**. Generate transcriptions, summaries, and sentiment assessments from conversation audio recordings.
 
 Start with a template or create a custom analyzer to meet your specific business needs.
 

articles/ai-services/content-understanding/index.yml

@@ -20,7 +20,7 @@ landingContent:
           - text: What is Azure AI Content Understanding?
             url: overview.md
           - text: Azure AI Content Understanding REST API
-            url: /rest/api/aiservices/
+            url: /rest/api/contentunderstanding/operation-groups?view=rest-contentunderstanding-2024-12-01-preview&preserve-view=true
           - text: FAQ
             url: faq.yml
 
@@ -58,7 +58,7 @@ landingContent:
       - linkListType: whats-new
         links:
         - text: "What's new in Azure AI Content Understanding?"
-          url: whats-new.md         
+          url: whats-new.md
 
   - title: Reference
     linkLists:
@@ -68,6 +68,6 @@ landingContent:
             url: service-limits.md
           - text: Language and region support
             url: language-region-support.md
-            
 
-  
+
+

articles/ai-services/content-understanding/quickstart/use-ai-foundry.md

@@ -35,7 +35,7 @@ To follow is an example of building an analyzer to extract key data from an invo
 
 1. Upload a sample file of an invoice document or any other data relevant to your scenario.
 
-  :::image type="content" source="../media/analyzer-template/define-schema-upload.png" alt-text="Screenshot of upload step in user experience.":::
+   :::image type="content" source="../media/analyzer-template/define-schema-upload.png" alt-text="Screenshot of upload step in user experience.":::
 
 1. Content Understanding suggests analyzer templates based on your content type. For this example, select **Document analysis** and build your own schema tailored to the invoice scenario. When using your own data, select the analyzer template that best fits your needs, or create your own. See Analyzer templates for a full list of available templates.
 
@@ -122,5 +122,5 @@ To follow are several of our video analyzer templates.
 
 ## Next steps
 
-* In this quickstart, you learned how to create an analyzer in Azure AI Foundry. To use [REST API](/rest/api/aiservices/), *see* the [REST API quickstart](use-rest-api.md).
+* In this quickstart, you learned how to create an analyzer in Azure AI Foundry. To use [REST API](/rest/api/contentunderstanding/operation-groups?view=rest-contentunderstanding-2024-12-01-preview&preserve-view=true), *see* the [REST API quickstart](use-rest-api.md).
 

articles/ai-services/content-understanding/quickstart/use-rest-api.md

@@ -12,13 +12,13 @@ ms.date: 11/19/2024
 
 # Quickstart: Azure AI Content Understanding REST APIs
 
-* Start using the latest preview version of the Azure AI Content Understanding [REST API (2024-12-01-preview)](/rest/api/aiservices/).
+* Start using the latest preview version of the Azure AI Content Understanding [REST API (2024-12-01-preview)](/rest/api/contentunderstanding/operation-groups?view=rest-contentunderstanding-2024-12-01-preview&preserve-view=true).
 
 * Azure AI Content Understanding is a new generative AI-based [**Azure AI Service**](../../what-are-ai-services.md) that analyzes files of any modality (documents, images, videos, and audio) and extracts structured output in user-defined field formats.
 
 * Integrate the Content Understanding service into your workflows and applications easily by calling our REST APIs.
 
-* This quickstart guides you through using the [Content Understanding REST API](/rest/api/aiservices/) to create a custom analyzer and extract content and fields from your input.
+* This quickstart guides you through using the [Content Understanding REST API](/rest/api/contentunderstanding/operation-groups?view=rest-contentunderstanding-2024-12-01-preview&preserve-view=true) to create a custom analyzer and extract content and fields from your input.
 
 ## Prerequisites
 
@@ -542,6 +542,6 @@ The 200 (`OK`) JSON response includes a `status` field indicating the status of
 
 ## Next steps 
 
-* In this quickstart, you learned how to call the [REST API](/rest/api/aiservices/) to create a custom analyzer. For a user experience, try [**Azure AI Foundry**](https://ai.azure.com/). 
+* In this quickstart, you learned how to call the [REST API](/rest/api/contentunderstanding/operation-groups?view=rest-contentunderstanding-2024-12-01-preview&preserve-view=true) to create a custom analyzer. For a user experience, try [**Azure AI Foundry**](https://ai.azure.com/). 
 
 

articles/ai-services/content-understanding/service-limits.md

@@ -92,7 +92,7 @@ The following limits apply as of version 2024-12-01-preview.
 Classification fields can be defined to return either a single category (single-label classification) or multiple categories (multi-label classification).
 
 * **Single-label classification**: Defined using a string field with the `classify` method. It can be a top-level basic field or a subfield within a group or table.
-* **Multi-label classification**: Represented as a list of string fields with the `classify` method. In the [REST API](/rest/api/aiservices/), `method=classify` and `enum` are specified on the inner string field and can only be a top-level field.
+* **Multi-label classification**: Represented as a list of string fields with the `classify` method. In the [REST API](/rest/api/contentunderstanding/operation-groups?view=rest-contentunderstanding-2024-12-01-preview&preserve-view=true), `method=classify` and `enum` are specified on the inner string field and can only be a top-level field.
 
 *Note: Document analyzers currently don't support classification fields.*
 

articles/ai-services/content-understanding/toc.yml

@@ -46,9 +46,15 @@ items:
   - name: Video
     href: video/overview.md
     displayName: video, audio, voice, recognition, synthesis, speaker, identification, verification, diarization, transcription, translation, language, understanding, sentiment, analysis, emotion, detection, pronunciation, model
-- Name: Reference
+- name: Responsible AI
+  items:
+    - name: Transparency note
+      href: /legal/cognitive-services/content-understanding/transparency-note?toc=/azure/ai-services/content-understanding/toc.json&bc=/azure/ai-services/content-understanding/breadcrumb/toc.json
+    - name: Data, privacy, and security
+      href: /legal/cognitive-services/content-understanding/data-privacy?toc=/azure/ai-services/content-understanding/toc.json&bc=/azure/ai-services/content-understanding/breadcrumb/toc.json
+- name: Reference
   items:
   - name: REST API
     displayName: quota, tiers, throttle, max, adjustments, requests, support, ocr
-    href: /rest/api/aiservices/
+    href: /rest/api/contentunderstanding/operation-groups?view=rest-contentunderstanding-2024-12-01-preview&preserve-view=true