Updated NDS sample

Author: jboback

articles/ai-services/language-service/named-entity-recognition/how-to-call.md

@@ -1,7 +1,7 @@
 ---
 title: How to perform Named Entity Recognition (NER)
 titleSuffix: Azure AI services
-description: This article will show you how to extract named entities from text.
+description: This article shows you how to extract named entities from text.
 #services: cognitive-services
 author: jboback
 manager: nitinme
@@ -37,7 +37,7 @@ The API attempts to detect the [defined entity categories](concepts/named-entity
 
 ## Getting NER results
 
-When you get results from NER, you can stream the results to an application or save the output to a file on the local system. The API response will include [recognized entities](concepts/named-entity-categories.md), including their categories and subcategories, and confidence scores. 
+When you get results from NER, you can stream the results to an application or save the output to a file on the local system. The API response includes [recognized entities](concepts/named-entity-categories.md), including their categories and subcategories, and confidence scores. 
 
 ## Select which entities to be returned
 
@@ -46,7 +46,7 @@ The API attempts to detect the [defined entity types and tags](concepts/named-en
 **Input:**
 
 > [!NOTE]
-> In this example, it returns only the **Location** entity type.
+> In this example, it returns only the **"Location"** entity type.
 
 ```bash
 {
@@ -110,13 +110,13 @@ Using these parameters we can successfully filter on only `Location` entity type
 
 ## Additional output attributes
 
-In order to provide users with more insight into an entity's types and provide increased usability, NER supports these additional attributes in the output:
+In order to provide users with more insight into an entity's types and provide increased usability, NER supports these attributes in the output:
 
 |Name of the attribute|Type        |Definition                               |
 |---------------------|------------|-----------------------------------------|
-|`type`               |String      |The most specific type of detected entity.<br><br>For example, “Seattle” is a City, a GPE (Geo Political Entity) and a “Location”. The most granular classification for “Seattle” is that it is a “City”. The type would be “City” for the text “Seattle".|
-|`tags`               |List (tags) |A list of tag objects which expresses the affinity of the detected entity to a hierarchy or any other grouping.<br><br>A tag contains two fields:<br>1. `name`: A unique name for the tag.<br>2. `confidenceScore`: The associated confidence score for a tag ranging from  <br><br>This unique tagName will be used to filter in the `inclusionList` and `exclusionList` parameters  
-|`metadata`           |Object      |Metadata is an object containing additional data about the entity type detected. It changes based on the field `metadataKind`.
+|`type`               |String      |The most specific type of detected entity.<br><br>For example, “Seattle” is a `City`, a `GPE` (Geo Political Entity) and a `Location`. The most granular classification for “Seattle” is that it is a `City`. The type would be “City” for the text “Seattle".|
+|`tags`               |List (tags) |A list of tag objects which expresses the affinity of the detected entity to a hierarchy or any other grouping.<br><br>A tag contains two fields:<br>1. `name`: A unique name for the tag.<br>2. `confidenceScore`: The associated confidence score for a tag ranging from  <br><br>This unique tagName is be used to filter in the `inclusionList` and `exclusionList` parameters  
+|`metadata`           |Object      |Metadata is an object containing more data about the entity type detected. It changes based on the field `metadataKind`.
 
 ## Sample output
 

articles/ai-services/language-service/native-document-support/use-native-documents.md

@@ -169,32 +169,39 @@ For this quickstart, you need a **source document** uploaded to your **source co
   ***Request sample***
 
 ```json
-{
-    "displayName": "Extracting Location & US Region",
-    "analysisInput": {
-        "documents": [
-            {
-                "language": "en-US",
-                "id": "Output-excel-file",
-                "source": {
-                    "location": "{your-source-blob-with-SAS-URL}"
-                },
-                "target": {
-                    "location": "{your-target-container-with-SAS-URL}"
-                }
+{ 
+    "displayName": "Document PII Redaction example", 
+    "analysisInput": { 
+        "documents": [ 
+            { 
+                "language": "en-US", 
+                "id": "Output-1", 
+                "source": { 
+                    "location": "{your-source-blob-with-SAS-URL}" 
+                }, 
+                "target": { 
+                    "location": "{your-target-container-with-SAS-URL}" 
+                } 
             } 
-        ]
-    },
-    "tasks": [
-        {
-            "kind": "PiiEntityRecognition",
-            "parameters":{
-                "excludePiiCategories" : ["PersonType", "Category2", "Category3"],
-                "redactionPolicy": "UseRedactionCharacterWithRefId" 
-            }
-        }
-    ]
-}
+        ] 
+    }, 
+    "tasks": [ 
+        { 
+            "kind": "PiiEntityRecognition", 
+            "taskName": "Redact PII Task 1", 
+            "parameters": { 
+                "redactionPolicy": { 
+                    "policyKind": "entityMask"  // Optional. Defines redactionPolicy; changes behavior based on value. Options: noMask, characterMask (default), and entityMask. 
+                }, 
+                "piiCategories": [ 
+                    "Person", 
+                    "Organization" 
+                ], 
+                "excludeExtractionData": false  // Default is false. If true, only the redacted document is stored, without extracted entities data. 
+            } 
+        } 
+    ] 
+} 
 ```
 
 * The source `location` value is the SAS URL for the **source document (blob)**, not the source container SAS URL.