Merge pull request #5379 from aahill/lang-freshness

prmerger-automator[bot] · web-flow · commit 6d9ee2caa3b1 · 2025-06-04T23:18:26.000Z
acrolinx update
diff --git a/articles/ai-services/language-service/conversational-language-understanding/concepts/best-practices.md b/articles/ai-services/language-service/conversational-language-understanding/concepts/best-practices.md
@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: best-practice
-ms.date: 11/21/2024
+ms.date: 06/04/2025
 ms.author: lajanuar
 ms.custom: language-service-clu
 ---
@@ -112,7 +112,7 @@ If you enable this feature, the utterance count of your training set increases.
 
 ## Address model overconfidence
 
-Customers can use the LoraNorm traning configuration version if the model is being incorrectly overconfident. An example of this behavior can be like the following scenario where the model predicts the incorrect intent with 100% confidence. This score makes the confidence threshold project setting unusable.
+Customers can use the LoraNorm training configuration version if the model is being incorrectly overconfident. An example of this behavior can be like the following scenario where the model predicts the incorrect intent with 100% confidence. This score makes the confidence threshold project setting unusable.
 
 | Text |	Predicted intent |	Confidence score |
 |----|----|----|
@@ -272,6 +272,6 @@ After the request is sent, you can track the progress of the training job in Lan
 
 Caveats:
 
-- The None score threshold for the app (confidence threshold below which `topIntent` is marked as `None`) when you use this training configuration should be set to 0. This setting is used because this new training configuration attributes a certain portion of the in-domain probabilities to out of domain so that the model isn't incorrectly overconfident about in-domain utterances. As a result, users might see slightly reduced confidence scores for in-domain utterances as compared to the prod training configuration.
+- The None score threshold for the app (confidence threshold below which `topIntent` is marked as `None`) when you use this training configuration should be set to 0. This setting is used because this new training configuration attributes a certain portion of the in-domain probabilities to out of domain so that the model isn't incorrectly overconfident about in-domain utterances. As a result, users might see slightly reduced confidence scores for in-domain utterances as compared to the production training configuration.
 - We don't recommend this training configuration for apps with only two intents, such as `IntentA` and `None`, for example.
 - We don't recommend this training configuration for apps with a low number of utterances per intent. We highly recommend a minimum of 25 utterances per intent.
diff --git a/articles/ai-services/language-service/conversational-language-understanding/language-support.md b/articles/ai-services/language-service/conversational-language-understanding/language-support.md
@@ -11,7 +11,7 @@ ms.author: lajanuar
 ms.custom: language-service-clu
 ---
 
-# Language support for conversational language understanding
+# Language support for Conversational Language Understanding (CLU)
 
 Use this article to learn about the languages currently supported by CLU feature.
 
diff --git a/articles/ai-services/language-service/conversational-language-understanding/tutorials/bot-framework.md b/articles/ai-services/language-service/conversational-language-understanding/tutorials/bot-framework.md
@@ -5,10 +5,9 @@ keywords: conversational language understanding, bot framework, bot, language un
 author: laujan
 ms.author: lajanuar
 manager: nitinme
-ms.reviewer: cahann, hazemelh
 ms.service: azure-ai-language
 ms.topic: tutorial
-ms.date: 11/21/2024
+ms.date: 06/04/2025
 ---
 
 # Integrate conversational language understanding with Bot Framework
diff --git a/articles/ai-services/language-service/language-detection/how-to/call-api.md b/articles/ai-services/language-service/language-detection/how-to/call-api.md
@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: how-to
-ms.date: 11/21/2024
+ms.date: 06/04/2025
 ms.author: lajanuar
 ms.custom: language-service-language-detection
 ---
@@ -49,7 +49,7 @@ Analysis is performed upon receipt of the request. Using the language detection
 
 When you get results from language detection, you can stream the results to an application or save the output to a file on the local system.
 
-Language detection will return one predominant language for each document you submit, along with it's [ISO 639-1](https://www.iso.org/standard/22109.html) name, a human-readable name, a confidence score, script name and script code according to the [ISO 15924 standard](https://wikipedia.org/wiki/ISO_15924). A positive score of 1 indicates the highest possible confidence level of the analysis.
+Language detection will return one predominant language for each document you submit, along with it's [ISO 639-1](https://www.iso.org/standard/22109.html) name, a human-readable name, a confidence score, script name, and script code according to the [ISO 15924 standard](https://wikipedia.org/wiki/ISO_15924). A positive score of 1 indicates the highest possible confidence level of the analysis.
 
 
 ### Ambiguous content
diff --git a/articles/ai-services/language-service/personally-identifiable-information/language-support.md b/articles/ai-services/language-service/personally-identifiable-information/language-support.md
@@ -6,14 +6,14 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 11/21/2024
+ms.date: 06/04/2025
 ms.author: lajanuar
 ms.custom: language-service-pii, build-2024
 ---
 
 # Personally Identifiable Information (PII) detection language support 
 
-Use this article to learn which natural languages are supported by the text PII, document PII, and conversation PII features of Azure AI Language.
+Use this article to learn which natural languages are supported by the text PII, document PII, and conversation PII features of Azure AI Language Service.
 # [Text PII](#tab/text)
 
 ## Text PII language support
diff --git a/articles/ai-services/language-service/question-answering/concepts/confidence-score.md b/articles/ai-services/language-service/question-answering/concepts/confidence-score.md
@@ -1,20 +1,20 @@
 ---
-title: Confidence score - custom question answering
+title: Confidence score - Custom question answering
 titleSuffix: Azure AI services
-description: When a user query is matched against a knowledge base, custom question answering returns relevant answers, along with a confidence score.
+description: When a user query is matched against a knowledge base, Custom question answering returns relevant answers, along with a confidence score.
 #services: cognitive-services
 manager: nitinme
 author: laujan
 ms.author: lajanuar
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 11/21/2024
+ms.date: 06/04/2025
 ms.custom: language-service-question-answering
 ---
 
 # Confidence score
 
-When a user query is matched against a project (also known as a knowledge base), custom question answering returns relevant answers, along with a confidence score. This score indicates the confidence that the answer is the right match for the given user query.
+When a user query is matched against a project (also known as a knowledge base), Custom question answering returns relevant answers, along with a confidence score. This score indicates the confidence that the answer is the right match for the given user query.
 
 The confidence score is a number between 0 and 100. A score of 100 is likely an exact match, while a score of 0 means, that no matching answer was found. The higher the score- the greater the confidence in the answer. For a given query, there could be multiple answers returned. In that case, the answers are returned in order of decreasing confidence score.
 
@@ -31,7 +31,7 @@ The following table indicates typical confidence associated for a given score.
 
 ## Choose a score threshold
 
-The table above shows the range of scores that can occur when querying with custom question answering. However, since every project is different, and has different types of words, intents, and goals- we recommend you test and choose the threshold that best works for you. By default the threshold is set to `0`, so that all possible answers are returned. The recommended threshold that should work for most projects, is **50**.
+The table above shows the range of scores that can occur when querying with Custom question answering. However, since every project is different, and has different types of words, intents, and goals- we recommend you test and choose the threshold that best works for you. By default the threshold is set to `0`, so that all possible answers are returned. The recommended threshold that should work for most projects, is **50**.
 
 When choosing your threshold, keep in mind the balance between **Accuracy** and **Coverage**, and adjust your threshold based on your requirements.
 
diff --git a/articles/ai-services/language-service/question-answering/tutorials/active-learning.md b/articles/ai-services/language-service/question-answering/tutorials/active-learning.md
@@ -1,11 +1,11 @@
 ---
 title: Enrich your project with active learning
-description: In this tutorial, learn how to enrich your custom question answering projects with active learning
+description: In this tutorial, learn how to enrich your Custom question answering projects with active learning
 ms.service: azure-ai-language
 ms.topic: tutorial
 author: laujan
 ms.author: lajanuar
-ms.date: 11/21/2024
+ms.date: 06/04/2025
 ms.custom: language-service-question-answering
 ---
 
@@ -20,13 +20,13 @@ In this tutorial, you learn how to:
 > * Accept/reject active learning suggestions
 > * Add alternate questions
 
-This tutorial shows you how to enhance your custom question answering project with active learning. If you notice that customers are asking questions that are not covered in your project, they may be paraphrased variations of questions.
+This tutorial shows you how to enhance your Custom question answering project with active learning. If you notice that customers are asking questions that are not covered in your project, they may be paraphrased variations of questions.
 
 These variations, when added as alternate questions to the relevant question answer pair, help to optimize the project to answer real world user queries. You can manually add alternate questions to question answer pairs through the editor. At the same time, you can also use the active learning feature to generate active learning suggestions based on user queries. The active learning feature, however, requires that the project receives regular user traffic to generate suggestions.
 
 ## Use active learning
 
-Active learning is turned on by default for custom question answering enabled resources.
+Active learning is turned on by default for Custom question answering enabled resources.
 
 To try out active learning suggestions, you can import the following file as a new project: [SampleActiveLearning.tsv](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/qna-maker/knowledge-bases/SampleActiveLearning.tsv).
 
diff --git a/articles/ai-services/language-service/question-answering/tutorials/guided-conversations.md b/articles/ai-services/language-service/question-answering/tutorials/guided-conversations.md
@@ -5,7 +5,7 @@ ms.service: azure-ai-language
 ms.topic: tutorial
 author: laujan
 ms.author: lajanuar
-ms.date: 11/21/2024
+ms.date: 06/04/2025
 ms.custom: language-service-question-answering
 ---
 
@@ -63,7 +63,7 @@ Using the editor, we add a new QnA pair with a follow-up prompt by clicking on *
     > [!div class="mx-imgBorder"]
     > [ ![Screenshot of UI with "add a follow-up prompt" highlighted in a red box]( ../media/guided-conversations/add-prompts.png) ]( ../media/guided-conversations/add-prompts.png#lightbox)
     
-    We provide **Check Compatibility** as the “Display text” for the prompt and try to link it to a QnA. Since, no related QnA pair is available to link to the prompt, when we search “Check your Surface Pen Compatibility”, we create a new question pair by clicking on **Create link to new pair** and select **Done**. Then select **Save changes**.
+    We provide **Check Compatibility** as the "Display text" for the prompt and try to link it to a QnA. Since, no related QnA pair is available to link to the prompt, when we search “Check your Surface Pen Compatibility”, we create a new question pair by clicking on **Create link to new pair** and select **Done**. Then select **Save changes**.
     
     > [!div class="mx-imgBorder"]
     > [ ![Screenshot of a question and answer about checking your surface pen compatibility]( ../media/guided-conversations/compatability-check.png) ]( ../media/guided-conversations/compatability-check.png#lightbox)
