Merge pull request #110681 from ctufts/chtufts/pythonQS1.0.0b4

Chtufts/python qs1.0.0b4

Author: v-shils

articles/cognitive-services/text-analytics/includes/quickstarts/python-sdk.md

@@ -2,7 +2,7 @@
 author: aahill
 ms.service: cognitive-services
 ms.topic: include
-ms.date: 03/24/2020
+ms.date: 04/8/2020
 ms.author: aahi
 ---
 
@@ -100,10 +100,11 @@ These code snippets show you how to do the following tasks with the Text Analyti
 Create a function to instantiate the `TextAnalyticsClient` object with your `key` AND `endpoint` created above. Then create a new client. 
 
 ```python
-from azure.ai.textanalytics import TextAnalyticsClient, TextAnalyticsApiKeyCredential
+from azure.ai.textanalytics import TextAnalyticsClient
+from azure.core.credentials import AzureKeyCredential
 
 def authenticate_client():
-    ta_credential = TextAnalyticsApiKeyCredential(key)
+    ta_credential = AzureKeyCredential(key)
     text_analytics_client = TextAnalyticsClient(
             endpoint=endpoint, credential=ta_credential)
     return text_analytics_client
@@ -131,8 +132,8 @@ Create a new function called `sentiment_analysis_example()` that takes the clien
 ```python
 def sentiment_analysis_example(client):
 
-    document = ["I had the best day of my life. I wish you were there with me."]
-    response = client.analyze_sentiment(inputs=document)[0]
+    documents = ["I had the best day of my life. I wish you were there with me."]
+    response = client.analyze_sentiment(documents = documents)[0]
     print("Document Sentiment: {}".format(response.sentiment))
     print("Overall scores: positive={0:.2f}; neutral={1:.2f}; negative={2:.2f} \n".format(
         response.confidence_scores.positive,
@@ -147,16 +148,15 @@ def sentiment_analysis_example(client):
             sentence.confidence_scores.neutral,
             sentence.confidence_scores.negative,
         ))
-
-            
+          
 sentiment_analysis_example(client)
 ```
 
 ### Output
 
 ```console
 Document Sentiment: positive
-Overall scores: positive=1.00; neutral=0.00; negative=0.00
+Overall scores: positive=1.00; neutral=0.00; negative=0.00 
 
 [Length: 30]
 Sentence 1 sentiment: positive
@@ -202,8 +202,8 @@ Create a new function called `language_detection_example()` that takes the clien
 ```python
 def language_detection_example(client):
     try:
-        document = ["Ce document est rédigé en Français."]
-        response = client.detect_language(inputs = document, country_hint = 'us')[0]
+        documents = ["Ce document est rédigé en Français."]
+        response = client.detect_language(documents = documents, country_hint = 'us')[0]
         print("Language: ", response.primary_language.name)
 
     except Exception as err:
@@ -240,8 +240,7 @@ Document ID: 3 , Language: Chinese_Simplified
 #### [Version 3.0-preview](#tab/version-3)
 
 > [!NOTE]
-> In version `3.0-preview`:
-> * NER includes separate methods for detecting personal information. 
+> In version `3.0-preview`: 
 > * Entity linking is a separate request than NER.
 
 Create a new function called `entity_recognition_example` that takes the client as an argument, then calls the `recognize_entities()` function and iterates through the results. The returned response object will contain the list of detected entities in `entity` if successful, and an `error` if not. For each detected entity, print its Category and Sub-Category if exists.
@@ -250,13 +249,13 @@ Create a new function called `entity_recognition_example` that takes the client
 def entity_recognition_example(client):
 
     try:
-        document = ["I had a wonderful trip to Seattle last week."]
-        result = client.recognize_entities(inputs= document)[0]
+        documents = ["I had a wonderful trip to Seattle last week."]
+        result = client.recognize_entities(documents = documents)[0]
 
         print("Named Entities:\n")
         for entity in result.entities:
             print("\tText: \t", entity.text, "\tCategory: \t", entity.category, "\tSubCategory: \t", entity.subcategory,
-                    "\n\tLength: \t", entity.grapheme_length, "\tConfidence Score: \t", round(entity.score, 2), "\n")
+                    "\n\tLength: \t", entity.grapheme_length, "\tConfidence Score: \t", round(entity.confidence_score, 2), "\n")
 
     except Exception as err:
         print("Encountered exception. {}".format(err))
@@ -275,35 +274,6 @@ Named Entities:
     Length:          9      Confidence Score:        0.8
 ```
 
-## Using NER to detect personal information
-
-Create a new function called `entity_pii_example()` that takes the client as an argument, then calls the `recognize_pii_entities()` function and gets the result. Then iterate through the results and print the entities.
-
-```python
-def entity_pii_example(client):
-
-        document = ["Insurance policy for SSN on file 123-12-1234 is here by approved."]
-
-
-        result = client.recognize_pii_entities(inputs= document)[0]
-        
-        print("Personally Identifiable Information Entities: ")
-        for entity in result.entities:
-            print("\tText: ",entity.text,"\tCategory: ", entity.category,"\tSubCategory: ", entity.subcategory)
-            print("\t\tLength: ", entity.grapheme_length, "\tScore: {0:.2f}".format(entity.score), "\n")
-        
-entity_pii_example(client)
-```
-
-### Output
-
-```console
-Personally Identifiable Information Entities:
-    Text:  123-12-1234      Category:  U.S. Social Security Number (SSN)    SubCategory:  None
-        Length:  11     Score: 0.85
-```
-
-
 ## Entity Linking
 
 Create a new function called `entity_linking_example()` that takes the client as an argument, then calls the `recognize_linked_entities()` function and iterates through the results. The returned response object will contain the list of detected entities in `entities` if successful, and an `error` if not. Since linked entities are uniquely identified, occurrences of the same entity are grouped under a `entity` object as a list of `match` objects.
@@ -312,12 +282,12 @@ Create a new function called `entity_linking_example()` that takes the client as
 def entity_linking_example(client):
 
     try:
-        document = ["""Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, 
+        documents = ["""Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, 
         to develop and sell BASIC interpreters for the Altair 8800. 
         During his career at Microsoft, Gates held the positions of chairman,
         chief executive officer, president and chief software architect, 
         while also being the largest individual shareholder until May 2014."""]
-        result = client.recognize_linked_entities(inputs= document)[0]
+        result = client.recognize_linked_entities(documents = documents)[0]
 
         print("Linked Entities:\n")
         for entity in result.entities:
@@ -326,7 +296,7 @@ def entity_linking_example(client):
             print("\tMatches:")
             for match in entity.matches:
                 print("\t\tText:", match.text)
-                print("\t\tScore: {0:.2f}".format(match.score), "\tLength: {}\n".format(match.grapheme_length))
+                print("\t\tConfidence Score: {0:.2f}".format(match.confidence_score), "\tLength: {}\n".format(match.grapheme_length))
 
     except Exception as err:
         print("Encountered exception. {}".format(err))
@@ -338,48 +308,47 @@ entity_linking_example(client)
 ```console
 Linked Entities:
 
-    Name:  Altair 8800      Id:  Altair 8800        Url:  https://en.wikipedia.org/wiki/Altair_8800
+    Name:  Altair 8800     Id:  Altair 8800     Url:  https://en.wikipedia.org/wiki/Altair_8800 
     Data Source:  Wikipedia
     Matches:
         Text: Altair 8800
-        Score: 0.78     Length: 11
+        Confidence Score: 0.00     Length: 11
 
-    Name:  Bill Gates       Id:  Bill Gates         Url:  https://en.wikipedia.org/wiki/Bill_Gates
+    Name:  Bill Gates     Id:  Bill Gates     Url:  https://en.wikipedia.org/wiki/Bill_Gates 
     Data Source:  Wikipedia
     Matches:
         Text: Bill Gates
-        Score: 0.55     Length: 10
+        Confidence Score: 0.00     Length: 10
 
         Text: Gates
-        Score: 0.55     Length: 5
+        Confidence Score: 0.00     Length: 5
 
-    Name:  Paul Allen       Id:  Paul Allen         Url:  https://en.wikipedia.org/wiki/Paul_Allen
+    Name:  Paul Allen     Id:  Paul Allen     Url:  https://en.wikipedia.org/wiki/Paul_Allen 
     Data Source:  Wikipedia
     Matches:
         Text: Paul Allen
-        Score: 0.53     Length: 10
+        Confidence Score: 0.00     Length: 10
 
-    Name:  Microsoft        Id:  Microsoft  Url:  https://en.wikipedia.org/wiki/Microsoft
+    Name:  Microsoft     Id:  Microsoft     Url:  https://en.wikipedia.org/wiki/Microsoft 
     Data Source:  Wikipedia
     Matches:
         Text: Microsoft
-        Score: 0.47     Length: 9
+        Confidence Score: 0.00     Length: 9
 
         Text: Microsoft
-        Score: 0.47     Length: 9
+        Confidence Score: 0.00     Length: 9
 
-    Name:  April 4  Id:  April 4    Url:  https://en.wikipedia.org/wiki/April_4
+    Name:  April 4     Id:  April 4     Url:  https://en.wikipedia.org/wiki/April_4 
     Data Source:  Wikipedia
     Matches:
         Text: April 4
-        Score: 0.25     Length: 7
+        Confidence Score: 0.00     Length: 7
 
-    Name:  BASIC    Id:  BASIC      Url:  https://en.wikipedia.org/wiki/BASIC
+    Name:  BASIC     Id:  BASIC     Url:  https://en.wikipedia.org/wiki/BASIC 
     Data Source:  Wikipedia
     Matches:
         Text: BASIC
-        Score: 0.28     Length: 5
-
+        Confidence Score: 0.00     Length: 5
 ```
 
 #### [Version 2.1](#tab/version-2)
@@ -443,9 +412,9 @@ Create a new function called `key_phrase_extraction_example()` that takes the cl
 def key_phrase_extraction_example(client):
 
     try:
-        document = ["My cat might need to see a veterinarian."]
+        documents = ["My cat might need to see a veterinarian."]
 
-        response = client.extract_key_phrases(inputs= document)[0]
+        response = client.extract_key_phrases(documents = documents)[0]
 
         if not response.is_error:
             print("\tKey Phrases:")