repair broken links

Author: laujan

articles/ai-services/language-service/custom-named-entity-recognition/overview.md

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 04/29/2025
+ms.date: 07/16/2025
 ms.author: lajanuar
 ms.custom: language-service-custom-ner
 ---
@@ -75,7 +75,7 @@ As you use custom NER, see the following reference documentation and samples for
 
 ## Responsible AI 
 
-An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Read the [transparency note for custom NER](/azure/ai-foundry/responsible-ai/language-service/cner-transparency-note) to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
+An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Read the [transparency note for custom NER](/azure/ai-foundry/responsible-ai/language-service/transparency-note) to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 

articles/ai-services/language-service/overview.md

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 06/21/2025
+ms.date: 07/16/2025
 ms.author: lajanuar
 ---
 
@@ -248,6 +248,6 @@ Use Language service containers to deploy API features on-premises. These Docker
 
 An AI system includes not only the technology, but also the people who use it, the people affected by it, and the deployment environment. Read the following articles to learn about responsible AI use and deployment in your systems:
 
-* [Transparency note for the Language service](/azure/ai-foundry/responsible-ai/text-analytics/transparency-note)
-* [Integration and responsible use](/azure/ai-foundry/responsible-ai/text-analytics/guidance-integration-responsible-use)
-* [Data, privacy, and security](/azure/ai-foundry/responsible-ai/text-analytics/data-privacy)
+* [Transparency note for the Language service](azure/ai-foundry/responsible-ai/language-service/transparency-note)
+* [Integration and responsible use](/azure/ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use)
+* [Data, privacy, and security](/azure/ai-foundry/responsible-ai/language-service/data-privacy)

articles/ai-services/language-service/personally-identifiable-information/how-to/redact-document-pii.md

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: how-to
-ms.date: 03/05/2025
+ms.date: 07/16/2025
 ms.author: lajanuar
 ms.custom: language-service-pii
 ---
@@ -65,11 +65,6 @@ A native document refers to the file format used to create the original document
     > macOS `curl -V`
     > Linux: `curl --version`
 
-* If cURL isn't installed, here are installation links for your platform:
-
-  * [Windows](https://curl.haxx.se/windows/).
-  * [Mac or Linux](https://learn2torials.com/thread/how-to-install-curl-on-mac-or-linux-(ubuntu)-or-windows).
-
 * An active [**Azure account**](https://azure.microsoft.com/free/cognitive-services/). If you don't have one, you can [**create a free account**](https://azure.microsoft.com/free/).
 
 * An [**Azure Blob Storage account**](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM). You also need to [create containers](#create-azure-blob-storage-containers) in your Azure Blob Storage account for your source and target files:

articles/ai-services/language-service/question-answering/concepts/best-practices.md

@@ -5,7 +5,7 @@ ms.service: azure-ai-language
 author: laujan
 ms.author: lajanuar
 ms.topic: conceptual
-ms.date: 06/21/2025
+ms.date: 07/16/2025
 ms.custom: language-service-question-answering
 ---
 
@@ -19,7 +19,7 @@ Custom question answering is continually improving the algorithms that extract q
 
 ## Creating good questions and answers
 
-We’ve used the following list of question and answer pairs as representation of a project to highlight best practices when authoring projects for custom question answering.
+We've used the following list of question and answer pairs as representation of a project to highlight best practices when authoring projects for custom question answering.
 
 | Question | Answer |
 |----------|----------|
@@ -39,23 +39,23 @@ Custom question answering employs a transformer-based ranker that takes care of
 
 The service can return the expected response for semantically similar queries such as:
 
-“How much is Microsoft stock worth?
-“How much is Microsoft share value?”
-“How much does a Microsoft share cost?”
-“What is the market value of a Microsoft stock?”
-“What is the market value of a Microsoft share?”
+"How much is Microsoft stock worth?
+"How much is Microsoft share value?"
+"How much does a Microsoft share cost?"
+"What is the market value of a Microsoft stock?"
+"What is the market value of a Microsoft share?"
 
-However, it’s important to understand that the confidence score with which the system returns the correct response will vary based on the input query and how different it is from the original question answer pair.  
+However, it's important to understand that the confidence score with which the system returns the correct response will vary based on the input query and how different it is from the original question answer pair.  
 
-There are certain scenarios that require the customer to add an alternate question. When it’s already verified that for a particular query the correct answer isn’t returned despite being present in the project, we advise adding that query as an alternate question to the intended question answer pair.
+There are certain scenarios that require the customer to add an alternate question. When it's already verified that for a particular query the correct answer isn't returned despite being present in the project, we advise adding that query as an alternate question to the intended question answer pair.
 
 ### How many alternate questions per question answer pair is optimal?
 
 Users can add as many alternate questions as they want, but only first 5 will be considered for core ranking. However, the rest will be useful for exact match scenarios. It is also recommended to keep the different intent/distinct alternate questions at the top for better relevance and score.
 
 Semantic understanding in custom question answering should be able to take care of similar alternate questions.
 
-The return on investment will start diminishing once you exceed 10 questions. Even if you’re adding more than 10 alternate questions, try to make the initial 10 questions as semantically dissimilar as possible so that all kinds of intents for the answer are captured by these 10 questions.  For the project at the beginning of this section, in question answer pair #1, adding alternate questions such as “How can I buy a car”, “I wanna buy a car” aren’t required. Whereas adding alternate questions such as “How to purchase a car”, “What are the options of buying a vehicle” can be useful.
+The return on investment will start diminishing once you exceed 10 questions. Even if you're adding more than 10 alternate questions, try to make the initial 10 questions as semantically dissimilar as possible so that all kinds of intents for the answer are captured by these 10 questions.  For the project at the beginning of this section, in question answer pair #1, adding alternate questions such as "How can I buy a car", "I wanna buy a car" aren't required. Whereas adding alternate questions such as "How to purchase a car", "What are the options of buying a vehicle" can be useful.
 
 ### When to add synonyms to a project?
 
@@ -67,17 +67,17 @@ For better relevance, you need to provide a list of acronyms that the end user i
 * `ID` – Identification
 * `ETA` – Estimated time of Arrival
 
-Other than acronyms, if you think your words are similar in context of a particular domain and generic language models won’t consider them similar, it’s better to add them as synonyms. For instance, if an auto company producing a car model X receives queries such as “my car’s audio isn’t working” and the project has questions on “fixing audio for car X”, then we need to add ‘X’ and ‘car’ as synonyms.
+Other than acronyms, if you think your words are similar in context of a particular domain and generic language models won't consider them similar, it's better to add them as synonyms. For instance, if an auto company producing a car model X receives queries such as "my car's audio isn't working" and the project has questions on "fixing audio for car X", then we need to add 'X' and 'car' as synonyms.
 
-The transformer-based model already takes care of most of the common synonym cases, for example: `Purchase – Buy`, `Sell - Auction`, `Price – Value`. For another example, consider the following question answer pair: Q: “What is the price of Microsoft Stock?” A: “$200”.  
+The transformer-based model already takes care of most of the common synonym cases, for example: `Purchase – Buy`, `Sell - Auction`, `Price – Value`. For another example, consider the following question answer pair: Q: "What is the price of Microsoft Stock?" A: "$200".  
 
-If we receive user queries like “Microsoft stock value”,” Microsoft share value”, “Microsoft stock worth”, “Microsoft share worth”, “stock value”, etc., you should be able to get the correct answer even though these queries have words like "share", "value", and "worth", which aren’t originally present in the project.
+If we receive user queries like "Microsoft stock value"," Microsoft share value", "Microsoft stock worth", "Microsoft share worth", "stock value", etc., you should be able to get the correct answer even though these queries have words like "share", "value", and "worth", which aren't originally present in the project.
 
 Special characters are not allowed in synonyms.
 
 ### How are lowercase/uppercase characters treated?
 
-Question answering takes casing into account but it's intelligent enough to understand when it’s to be ignored. You shouldn’t be seeing any perceivable difference due to wrong casing.
+Question answering takes casing into account but it's intelligent enough to understand when it's to be ignored. You shouldn't be seeing any perceivable difference due to wrong casing.
 
 ### How are question answer pairs prioritized for multi-turn questions?
 
@@ -89,7 +89,7 @@ Accents are supported for all major European languages. If the query has an inco
 
 ### How is punctuation in a user query treated?
 
-Punctuation is ignored in a user query before sending it to the ranking stack. Ideally it shouldn’t impact the relevance scores. Punctuation that is ignored is as follows:  `,?:;\"'(){}[]-+。./!*؟`
+Punctuation is ignored in a user query before sending it to the ranking stack. Ideally it shouldn't impact the relevance scores. Punctuation that is ignored is as follows:  `,?:;\"'(){}[]-+。./!*؟`
 
 ## Chit-Chat
 
@@ -109,7 +109,7 @@ Chit-chat is supported for several predefined personalities:
 |Caring |[qna_chitchat_caring.tsv](https://qnamakerstore.blob.core.windows.net/qnamakerdata/editorial/qna_chitchat_caring.tsv) |
 |Enthusiastic |[qna_chitchat_enthusiastic.tsv](https://qnamakerstore.blob.core.windows.net/qnamakerdata/editorial/qna_chitchat_enthusiastic.tsv) |
 
-The responses range from formal to informal and irreverent. You should select the personality that is closest aligned with the tone you want for your bot. You can view the [datasets](https://github.com/Microsoft/BotBuilder-PersonalityChat/tree/master/CSharp/Datasets), and choose one that serves as a base for your bot, and then customize the responses.
+The responses range from formal to informal and irreverent. You should select the personality that is closest aligned with the tone you want for your bot. You can view the datasets, and choose one that serves as a base for your bot, and then customize the responses.
 
 ### Edit bot-specific questions
 
@@ -131,15 +131,15 @@ If you add your own chit-chat question answer pairs, make sure to add metadata s
 
 The custom question answering REST API uses both questions and the answer to search for best answers to a user's query.
 
-### Searching questions only when answer isn’t relevant
+### Searching questions only when answer isn't relevant
 
 Use the [`RankerType=QuestionOnly`](#choosing-ranker-type) if you don't want to search answers.
 
-An example of this is when the project is a catalog of acronyms as questions with their full form as the answer. The value of the answer won’t help to search for the appropriate answer.
+An example of this is when the project is a catalog of acronyms as questions with their full form as the answer. The value of the answer won't help to search for the appropriate answer.
 
 ## Ranking/Scoring
 
-Make sure you’re making the best use of the supported ranking features. Doing so will improve the likelihood that a given user query is answered with an appropriate response.
+Make sure you're making the best use of the supported ranking features. Doing so will improve the likelihood that a given user query is answered with an appropriate response.
 
 ### Choosing a threshold
 
@@ -160,11 +160,11 @@ Alternate questions to improve the likelihood of a match with a user query. Alte
 
 ### Use metadata tags to filter questions and answers
 
-Metadata adds the ability for a client application to know it shouldn’t take all answers but instead to narrow down the results of a user query based on metadata tags. The project answer can differ based on the metadata tag, even if the query is the same. For example, *"where is parking located"* can have a different answer if the location of the restaurant branch is different - that is, the metadata is *Location: Seattle* versus *Location: Redmond*.
+Metadata adds the ability for a client application to know it shouldn't take all answers but instead to narrow down the results of a user query based on metadata tags. The project answer can differ based on the metadata tag, even if the query is the same. For example, *"where is parking located"* can have a different answer if the location of the restaurant branch is different - that is, the metadata is *Location: Seattle* versus *Location: Redmond*.
 
 ### Use synonyms
 
-While there’s some support for synonyms in the English language, use case-insensitive [word alterations](../tutorials/adding-synonyms.md) to add synonyms to keywords that take different forms.
+While there's some support for synonyms in the English language, use case-insensitive [word alterations](../tutorials/adding-synonyms.md) to add synonyms to keywords that take different forms.
 
 |Original word|Synonyms|
 |--|--|
@@ -191,7 +191,7 @@ Custom question answering allows users to collaborate on a project. Users need a
 
 ## Active learning
 
-[Active learning](../tutorials/active-learning.md) does the best job of suggesting alternative questions when it has a wide range of quality and quantity of user-based queries. It’s important to allow client-applications' user queries to participate in the active learning feedback loop without censorship. Once questions are suggested in Language Studio, you can review and accept or reject those suggestions.
+[Active learning](../tutorials/active-learning.md) does the best job of suggesting alternative questions when it has a wide range of quality and quantity of user-based queries. It's important to allow client-applications' user queries to participate in the active learning feedback loop without censorship. Once questions are suggested in Language Studio, you can review and accept or reject those suggestions.
 
 ## Next steps
 

articles/ai-services/language-service/question-answering/overview.md

@@ -6,7 +6,7 @@ author: laujan
 ms.author: lajanuar
 recommendations: false
 ms.topic: overview
-ms.date: 03/24/2025
+ms.date: 07/16/2025
 keywords: "qna maker, low code chat bots, multi-turn conversations"
 ms.custom: language-service-question-answering
 ---
@@ -35,7 +35,7 @@ This documentation contains the following article types:
 * **When you want to provide the same answer to a request, question, or command** - when different users submit the same question, the same answer is returned.
 * **When you want to filter static information based on meta-information** - add [metadata](./tutorials/multiple-domains.md) tags to provide additional filtering options relevant to your client application's users and the information. Common metadata information includes [chit-chat](./how-to/chit-chat.md), content type or format, content purpose, and content freshness. <!--TODO: Fix Link-->
 * **When you want to manage a bot conversation that includes static information** - your project takes a user's conversational text or command and answers it. If the answer is part of a pre-determined conversation flow, represented in your project with [multi-turn context](./tutorials/guided-conversations.md), the bot can easily provide this flow.
-* **When you want to use an agent to get an exact answer** - Use the [exact question answering](https://aka.ms/exact-answer-agent-template) agent template answers high-value predefined questions deterministically to ensure consistent and accurate responses or the [intent routing](https://aka.ms/intent-triage-agent-template) agent template, which detects user intent and provides exact answering. Perfect for deterministically intent routing and exact question answering with human control.
+* **When you want to use an agent to get an exact answer** - Use the [exact question answering](https://github.com/azure-ai-foundry/foundry-samples/tree/main/samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/customer-service-agent) agent template answers high-value predefined questions deterministically to ensure consistent and accurate responses or the [intent routing](https://github.com/azure-ai-foundry/foundry-samples/tree/main/samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/intent-routing-agent) agent template, which detects user intent and provides exact answering. Perfect for deterministically intent routing and exact question answering with human control.
 
 ## What is a project?
 

articles/ai-services/language-service/sentiment-opinion-mining/how-to/call-api.md

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: how-to
-ms.date: 05/23/2025
+ms.date: 07/16/2025
 ms.author: lajanuar
 ms.custom: language-service-sentiment-opinion-mining
 ---
@@ -28,7 +28,7 @@ The labels are *positive*, *negative*, and *neutral*. At the document level, the
 | At least one `negative` sentence and at least one `positive` sentence are in the document.    | `mixed`                 |
 | All sentences in the document are `neutral`.                                                  | `neutral`               |
 
-Confidence scores range from 1 to 0. Scores closer to 1 indicate a higher confidence in the label's classification, while lower scores indicate lower confidence. For each document or each sentence, the predicted scores associated with the labels (positive, negative, and neutral) add up to 1. For more information, see the [Responsible AI transparency note](/azure/ai-foundry/responsible-ai/text-analytics/transparency-note). 
+Confidence scores range from 1 to 0. Scores closer to 1 indicate a higher confidence in the label's classification, while lower scores indicate lower confidence. For each document or each sentence, the predicted scores associated with the labels (positive, negative, and neutral) add up to 1. For more information, see the [Responsible AI transparency note](/azure/ai-foundry/responsible-ai/language-service/transparency-note). 
 
 ## Opinion Mining