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适用于 .NET 的 Azure 认知服务文本分析客户端库 - 版本 5.3.0

项目
06/21/2023

文本分析是适用于语言的 Azure 认知服务的一部分，该服务是一项基于云的服务，提供自然语言处理 (NLP) 功能，用于理解和分析文本。此客户端库提供以下功能：

语言检测
情绪分析
关键短语提取
命名实体识别 (NER)
个人身份信息 (PII) 实体识别
实体链接
运行状况文本分析
自定义命名实体识别 (自定义 NER)
自定义文本分类
提取文本摘要
抽象文本摘要

入门

安装包

使用 NuGet 安装适用于 .NET 的 Azure 文本分析客户端库：

dotnet add package Azure.AI.TextAnalytics

此表显示了 SDK 版本与服务支持的 API 版本之间的关系：

请注意， 5.2.0 是面向 Azure 认知服务语言 API 的客户端库的第一个稳定版本，其中包括文本分析客户端库中的现有文本分析和自然语言处理功能。此外，服务 API 已从语义化更改为基于日期的版本控制。

SDK 版本	服务支持的 API 版本
5.3.X	3.0、3.1、2022-05-01、2023-04-01 (默认)
5.2.X	3.0、3.1、2022-05-01 (默认)
5.1.X	3.0、3.1（默认）
5.0.X	3.0
1.0.X	3.0

先决条件

一个 Azure 订阅。
现有的认知服务或语言服务资源。

创建认知服务资源或语言服务资源

适用于语言的 Azure 认知服务支持多服务和单服务访问。如果计划访问单个终结点和 API 密钥下的多个认知服务，请创建认知服务资源。若要仅访问语言服务的功能，请改为创建语言服务资源。

可以通过 Azure 门户创建任一资源，或者，也可以按照本文档中的步骤使用 Azure CLI 创建资源。

验证客户端

使用客户端库与服务的交互从创建 TextAnalyticsClient 类的实例开始。需要 终结点和API 密钥 或 TokenCredential 实例化客户端对象。有关使用认知服务进行身份验证的详细信息，请参阅对 Azure 认知服务的请求进行身份验证。

获取 API 密钥

可以从 Azure 门户中的endpoint认知服务资源或语言服务资源信息获取和 API key 。

或者，使用以下 Azure CLI 代码片段从语言服务资源获取 API 密钥。

az cognitiveservices account keys list --resource-group <your-resource-group-name> --name <your-resource-name>

`TextAnalyticsClient`使用 API 密钥凭据创建

获得 API 密钥的值后，请 AzureKeyCredential创建。这样，无需创建新客户端即可更新 API 密钥。

使用终结点和 AzureKeyCredential的值，可以创建 TextAnalyticsClient：

Uri endpoint = new("<endpoint>");
AzureKeyCredential credential = new("<apiKey>");
TextAnalyticsClient client = new(endpoint, credential);

`TextAnalyticsClient`使用 Azure Active Directory 凭据创建

本入门指南中的大多数示例中都使用了客户端 API 密钥身份验证，但也可以使用 Azure 标识库通过 Azure Active Directory 进行身份验证。请注意，区域终结点不支持 AAD 身份验证。为资源创建自定义子域，以使用此类身份验证。

若要使用如下所示的 DefaultAzureCredential 提供程序或 Azure SDK 提供的其他凭据提供程序，请安装 Azure.Identity 包：

dotnet add package Azure.Identity

还需要注册新的 AAD 应用程序，并通过向服务主体分配角色来"Cognitive Services User"授予对语言服务的访问权限。

将 AAD 应用程序的客户端 ID、租户 ID 和客户端密码的值设置为环境变量：AZURE_CLIENT_ID、AZURE_TENANT_ID、AZURE_CLIENT_SECRET。

Uri endpoint = new("<endpoint>");
TextAnalyticsClient client = new(endpoint, new DefaultAzureCredential());

关键概念

`TextAnalyticsClient`

是TextAnalyticsClient开发人员使用文本分析客户端库的主要接口。它提供同步和异步操作来访问特定使用的文本分析，例如语言检测或关键短语提取。

输入

文档是语言服务中的预测模型要分析的单个输入单位。对 TextAnalyticsClient 的操作可能需要将单个文档或文档集合作为批处理进行分析。有关文档长度限制、最大批大小和支持的文本编码，请参阅此处。

对多个文档执行的操作

对于每个受支持的操作， TextAnalyticsClient 提供一个方法，该方法接受一批文档作为字符串，或一批 TextDocumentInput 或 DetectLanguageInput 对象。此方法允许调用方为每个文档提供唯一 ID、指示批中的文档以不同的语言编写，或提供有关文档语言的国家/地区提示。

注意： 建议在生产环境中使用批处理方法，因为它们允许发送包含多个文档的一个请求。这比为每个文档发送请求更高性能。

返回值

返回值（如 AnalyzeSentimentResult）是文本分析操作的结果，其中包含有关单个文档的预测。操作的返回值还可以选择性地包含有关文档及其处理方式的信息。

返回值集合

Return 值集合（如 AnalyzeSentimentResultCollection）是操作结果的集合，其中每个集合对应于输入批处理中提供的文档之一。文档及其结果在输入和结果集合中具有相同的索引。返回值还包含一个 HasError 属性，该属性允许标识为给定文档执行的操作是成功还是失败。可以选择包含有关文档批处理及其处理方式的信息。

Long-Running操作

对于执行时间较长的大型文档，这些操作作为 长时间运行的操作实现。长时间运行的操作包括发送到服务以启动操作的初始请求，然后每隔一段时间轮询服务以确定操作是否已完成或失败，如果操作成功，则获取结果。

对于 Azure SDK 中长时间运行的操作，客户端会公开返回 Start<operation-name>Operation<T> 或 PageableOperation<T>的方法。可以使用扩展方法 WaitForCompletionAsync() 等待操作完成并获取其结果。下面提供了一个示例代码片段，用于说明如何使用长时间运行的操作。

线程安全

我们保证所有客户端实例方法都是线程安全的，并且彼此独立 (准则) 。这可确保重用客户端实例的建议始终是安全的，即使在线程之间也是如此。

其他概念

示例

以下部分使用上面创建的提供多个代码片段client，并介绍了此客户端库中存在的main功能。尽管下面的大多数代码片段都使用同步服务调用，但请记住， Azure.AI.TextAnalytics 包支持同步和异步 API。

异步示例

异步检测语言
异步识别命名实体
异步分析医疗保健实体
异步运行多个操作

检测语言

运行预测模型以确定传入的文档或批量文档所使用的语言。

string document =
    "Este documento está escrito en un lenguaje diferente al inglés. Su objectivo es demostrar cómo"
    + " invocar el método de Detección de Lenguaje del servicio de Text Analytics en Microsoft Azure."
    + " También muestra cómo acceder a la información retornada por el servicio. Esta funcionalidad es"
    + " útil para los sistemas de contenido que recopilan texto arbitrario, donde el lenguaje no se conoce"
    + " de antemano. Puede usarse para detectar una amplia gama de lenguajes, variantes, dialectos y"
    + " algunos idiomas regionales o culturales.";

try
{
    Response<DetectedLanguage> response = client.DetectLanguage(document);
    DetectedLanguage language = response.Value;

    Console.WriteLine($"Detected language is {language.Name} with a confidence score of {language.ConfidenceScore}.");
}
catch (RequestFailedException exception)
{
    Console.WriteLine($"Error Code: {exception.ErrorCode}");
    Console.WriteLine($"Message: {exception.Message}");
}

有关使用生产推荐选项 DetectLanguageBatch 的示例，请参阅此处。

有关语言检测的概念性讨论，请参阅服务文档。

分析情绪

运行预测模型以确定传入的文档或批处理文档中包含的积极、消极、中性或混合情绪。

string document =
    "I had the best day of my life. I decided to go sky-diving and it made me appreciate my whole life so"
    + "much more. I developed a deep-connection with my instructor as well, and I feel as if I've made a"
    + "life-long friend in her.";

try
{
    Response<DocumentSentiment> response = client.AnalyzeSentiment(document);
    DocumentSentiment docSentiment = response.Value;

    Console.WriteLine($"Document sentiment is {docSentiment.Sentiment} with: ");
    Console.WriteLine($"  Positive confidence score: {docSentiment.ConfidenceScores.Positive}");
    Console.WriteLine($"  Neutral confidence score: {docSentiment.ConfidenceScores.Neutral}");
    Console.WriteLine($"  Negative confidence score: {docSentiment.ConfidenceScores.Negative}");
}
catch (RequestFailedException exception)
{
    Console.WriteLine($"Error Code: {exception.ErrorCode}");
    Console.WriteLine($"Message: {exception.Message}");
}

有关使用生产推荐选项 AnalyzeSentimentBatch 的示例，请参阅此处。

若要获取有关与产品/服务目标（也称为自然语言处理中的基于方面的情绪分析 (NLP) ）相关的观点的更精细的信息，请参阅此处使用观点挖掘进行情绪分析的示例。

有关情绪分析的概念性讨论，请参阅服务文档。

提取关键短语

运行模型以识别在传入的文档或一批文档中找到的重要短语集合。

string document =
    "My cat might need to see a veterinarian. It has been sneezing more than normal, and although my"
    + " little sister thinks it is funny, I am worried it has the cold that I got last week. We are going"
    + " to call tomorrow and try to schedule an appointment for this week. Hopefully it will be covered by"
    + " the cat's insurance. It might be good to not let it sleep in my room for a while.";

try
{
    Response<KeyPhraseCollection> response = client.ExtractKeyPhrases(document);
    KeyPhraseCollection keyPhrases = response.Value;

    Console.WriteLine($"Extracted {keyPhrases.Count} key phrases:");
    foreach (string keyPhrase in keyPhrases)
    {
        Console.WriteLine($"  {keyPhrase}");
    }
}
catch (RequestFailedException exception)
{
    Console.WriteLine($"Error Code: {exception.ErrorCode}");
    Console.WriteLine($"Message: {exception.Message}");
}

有关使用生产推荐选项 ExtractKeyPhrasesBatch 的示例，请参阅此处。

有关关键短语提取的概念性讨论，请参阅服务文档。

识别命名实体

运行预测模型以识别传入文档或一批文档中的命名实体集合，并将这些实体分类为人员、位置或组织等类别。有关可用类别的详细信息，请参阅文本分析命名实体类别。

string document =
    "We love this trail and make the trip every year. The views are breathtaking and well worth the hike!"
    + " Yesterday was foggy though, so we missed the spectacular views. We tried again today and it was"
    + " amazing. Everyone in my family liked the trail although it was too challenging for the less"
    + " athletic among us. Not necessarily recommended for small children. A hotel close to the trail"
    + " offers services for childcare in case you want that.";

try
{
    Response<CategorizedEntityCollection> response = client.RecognizeEntities(document);
    CategorizedEntityCollection entitiesInDocument = response.Value;

    Console.WriteLine($"Recognized {entitiesInDocument.Count} entities:");
    foreach (CategorizedEntity entity in entitiesInDocument)
    {
        Console.WriteLine($"  Text: {entity.Text}");
        Console.WriteLine($"  Offset: {entity.Offset}");
        Console.WriteLine($"  Length: {entity.Length}");
        Console.WriteLine($"  Category: {entity.Category}");
        if (!string.IsNullOrEmpty(entity.SubCategory))
            Console.WriteLine($"  SubCategory: {entity.SubCategory}");
        Console.WriteLine($"  Confidence score: {entity.ConfidenceScore}");
        Console.WriteLine();
    }
}
catch (RequestFailedException exception)
{
    Console.WriteLine($"Error Code: {exception.ErrorCode}");
    Console.WriteLine($"Message: {exception.Message}");
}

有关使用生产推荐选项 RecognizeEntitiesBatch 的示例，请参阅此处。

有关命名实体识别的概念性讨论，请参阅服务文档。

识别 PII 实体

运行预测模型以识别包含传入文档或一批文档中找到的个人身份信息的实体集合，并将这些实体分类为美国社会保险号、驾驶执照号或信用卡号等类别。

string document =
    "Parker Doe has repaid all of their loans as of 2020-04-25. Their SSN is 859-98-0987. To contact them,"
    + " use their phone number 800-102-1100. They are originally from Brazil and have document ID number"
    + " 998.214.865-68.";

try
{
    Response<PiiEntityCollection> response = client.RecognizePiiEntities(document);
    PiiEntityCollection entities = response.Value;

    Console.WriteLine($"Redacted Text: {entities.RedactedText}");
    Console.WriteLine();
    Console.WriteLine($"Recognized {entities.Count} PII entities:");
    foreach (PiiEntity entity in entities)
    {
        Console.WriteLine($"  Text: {entity.Text}");
        Console.WriteLine($"  Category: {entity.Category}");
        if (!string.IsNullOrEmpty(entity.SubCategory))
            Console.WriteLine($"  SubCategory: {entity.SubCategory}");
        Console.WriteLine($"  Confidence score: {entity.ConfidenceScore}");
        Console.WriteLine();
    }
}
catch (RequestFailedException exception)
{
    Console.WriteLine($"Error Code: {exception.ErrorCode}");
    Console.WriteLine($"Message: {exception.Message}");
}

有关使用生产推荐选项 RecognizePiiEntitiesBatch 的示例，请参阅此处。

有关支持的 PII 实体类型，请参阅服务文档。

识别链接实体

运行预测模型以识别在传入文档或一批文档中找到的实体集合，并包含将实体链接到已知知识库中的相应条目的信息。

string document =
    "Microsoft was founded by Bill Gates with some friends he met at Harvard. One of his friends, Steve"
    + " Ballmer, eventually became CEO after Bill Gates as well. Steve Ballmer eventually stepped down as"
    + " CEO of Microsoft, and was succeeded by Satya Nadella. Microsoft originally moved its headquarters"
    + " to Bellevue, Washington in Januaray 1979, but is now headquartered in Redmond.";

try
{
    Response<LinkedEntityCollection> response = client.RecognizeLinkedEntities(document);
    LinkedEntityCollection linkedEntities = response.Value;

    Console.WriteLine($"Recognized {linkedEntities.Count} entities:");
    foreach (LinkedEntity linkedEntity in linkedEntities)
    {
        Console.WriteLine($"  Name: {linkedEntity.Name}");
        Console.WriteLine($"  Language: {linkedEntity.Language}");
        Console.WriteLine($"  Data Source: {linkedEntity.DataSource}");
        Console.WriteLine($"  URL: {linkedEntity.Url}");
        Console.WriteLine($"  Entity Id in Data Source: {linkedEntity.DataSourceEntityId}");
        foreach (LinkedEntityMatch match in linkedEntity.Matches)
        {
            Console.WriteLine($"    Match Text: {match.Text}");
            Console.WriteLine($"    Offset: {match.Offset}");
            Console.WriteLine($"    Length: {match.Length}");
            Console.WriteLine($"    Confidence score: {match.ConfidenceScore}");
        }
        Console.WriteLine();
    }
}
catch (RequestFailedException exception)
{
    Console.WriteLine($"Error Code: {exception.ErrorCode}");
    Console.WriteLine($"Message: {exception.Message}");
}

有关使用生产推荐选项 RecognizeLinkedEntitiesBatch 的示例，请参阅此处。

有关实体链接的概念性讨论，请参阅服务文档。

异步检测语言

运行预测模型以确定传入的文档或批量文档所使用的语言。

string document =
    "Este documento está escrito en un lenguaje diferente al inglés. Su objectivo es demostrar cómo"
    + " invocar el método de Detección de Lenguaje del servicio de Text Analytics en Microsoft Azure."
    + " También muestra cómo acceder a la información retornada por el servicio. Esta funcionalidad es"
    + " útil para los sistemas de contenido que recopilan texto arbitrario, donde el lenguaje no se conoce"
    + " de antemano. Puede usarse para detectar una amplia gama de lenguajes, variantes, dialectos y"
    + " algunos idiomas regionales o culturales.";

try
{
    Response<DetectedLanguage> response = await client.DetectLanguageAsync(document);
    DetectedLanguage language = response.Value;

    Console.WriteLine($"Detected language is {language.Name} with a confidence score of {language.ConfidenceScore}.");
}
catch (RequestFailedException exception)
{
    Console.WriteLine($"Error Code: {exception.ErrorCode}");
    Console.WriteLine($"Message: {exception.Message}");
}

异步识别命名实体

string document =
    "We love this trail and make the trip every year. The views are breathtaking and well worth the hike!"
    + " Yesterday was foggy though, so we missed the spectacular views. We tried again today and it was"
    + " amazing. Everyone in my family liked the trail although it was too challenging for the less"
    + " athletic among us. Not necessarily recommended for small children. A hotel close to the trail"
    + " offers services for childcare in case you want that.";

try
{
    Response<CategorizedEntityCollection> response = await client.RecognizeEntitiesAsync(document);
    CategorizedEntityCollection entitiesInDocument = response.Value;

    Console.WriteLine($"Recognized {entitiesInDocument.Count} entities:");
    foreach (CategorizedEntity entity in entitiesInDocument)
    {
        Console.WriteLine($"  Text: {entity.Text}");
        Console.WriteLine($"  Offset: {entity.Offset}");
        Console.WriteLine($"  Length: {entity.Length}");
        Console.WriteLine($"  Category: {entity.Category}");
        if (!string.IsNullOrEmpty(entity.SubCategory))
            Console.WriteLine($"  SubCategory: {entity.SubCategory}");
        Console.WriteLine($"  Confidence score: {entity.ConfidenceScore}");
        Console.WriteLine();
    }
}
catch (RequestFailedException exception)
{
    Console.WriteLine($"Error Code: {exception.ErrorCode}");
    Console.WriteLine($"Message: {exception.Message}");
}

异步分析医疗保健实体

健康状况文本分析是一种容器化服务，它从非结构化文本（如医生的备注、出院摘要、临床文档和电子健康状况记录）中提取和标记相关医疗信息。有关详细信息，请参阅如何：使用文本分析实现健康。

string documentA =
    "RECORD #333582770390100 | MH | 85986313 | | 054351 | 2/14/2001 12:00:00 AM |"
    + " CORONARY ARTERY DISEASE | Signed | DIS |"
    + Environment.NewLine
    + " Admission Date: 5/22/2001 Report Status: Signed Discharge Date: 4/24/2001"
    + " ADMISSION DIAGNOSIS: CORONARY ARTERY DISEASE."
    + Environment.NewLine
    + " HISTORY OF PRESENT ILLNESS: The patient is a 54-year-old gentleman with a history of progressive"
    + " angina over the past several months. The patient had a cardiac catheterization in July of this"
    + " year revealing total occlusion of the RCA and 50% left main disease, with a strong family history"
    + " of coronary artery disease with a brother dying at the age of 52 from a myocardial infarction and"
    + " another brother who is status post coronary artery bypass grafting. The patient had a stress"
    + " echocardiogram done on July, 2001, which showed no wall motion abnormalities, but this was a"
    + " difficult study due to body habitus. The patient went for six minutes with minimal ST depressions"
    + " in the anterior lateral leads, thought due to fatigue and wrist pain, his anginal equivalent. Due"
    + " to the patient'sincreased symptoms and family history and history left main disease with total"
    + " occasional of his RCA was referred for revascularization with open heart surgery.";

string documentB = "Prescribed 100mg ibuprofen, taken twice daily.";

// Prepare the input of the text analysis operation. You can add multiple documents to this list and
// perform the same operation on all of them simultaneously.
List<string> batchedDocuments = new()
{
    documentA,
    documentB
};

// Perform the text analysis operation.
AnalyzeHealthcareEntitiesOperation operation = await client.AnalyzeHealthcareEntitiesAsync(WaitUntil.Completed, batchedDocuments);

Console.WriteLine($"The operation has completed.");
Console.WriteLine();

// View the operation status.
Console.WriteLine($"Created On   : {operation.CreatedOn}");
Console.WriteLine($"Expires On   : {operation.ExpiresOn}");
Console.WriteLine($"Id           : {operation.Id}");
Console.WriteLine($"Status       : {operation.Status}");
Console.WriteLine($"Last Modified: {operation.LastModified}");
Console.WriteLine();

// View the operation results.
await foreach (AnalyzeHealthcareEntitiesResultCollection documentsInPage in operation.Value)
{
    Console.WriteLine($"Analyze Healthcare Entities, model version: \"{documentsInPage.ModelVersion}\"");
    Console.WriteLine();

    foreach (AnalyzeHealthcareEntitiesResult documentResult in documentsInPage)
    {
        if (documentResult.HasError)
        {
            Console.WriteLine($"  Error!");
            Console.WriteLine($"  Document error code: {documentResult.Error.ErrorCode}");
            Console.WriteLine($"  Message: {documentResult.Error.Message}");
            continue;
        }

        Console.WriteLine($"  Recognized the following {documentResult.Entities.Count} healthcare entities:");
        Console.WriteLine();

        // View the healthcare entities that were recognized.
        foreach (HealthcareEntity entity in documentResult.Entities)
        {
            Console.WriteLine($"  Entity: {entity.Text}");
            Console.WriteLine($"  Category: {entity.Category}");
            Console.WriteLine($"  Offset: {entity.Offset}");
            Console.WriteLine($"  Length: {entity.Length}");
            Console.WriteLine($"  NormalizedText: {entity.NormalizedText}");
            Console.WriteLine($"  Links:");

            // View the entity data sources.
            foreach (EntityDataSource entityDataSource in entity.DataSources)
            {
                Console.WriteLine($"    Entity ID in Data Source: {entityDataSource.EntityId}");
                Console.WriteLine($"    DataSource: {entityDataSource.Name}");
            }

            // View the entity assertions.
            if (entity.Assertion is not null)
            {
                Console.WriteLine($"  Assertions:");

                if (entity.Assertion?.Association is not null)
                {
                    Console.WriteLine($"    Association: {entity.Assertion?.Association}");
                }

                if (entity.Assertion?.Certainty is not null)
                {
                    Console.WriteLine($"    Certainty: {entity.Assertion?.Certainty}");
                }

                if (entity.Assertion?.Conditionality is not null)
                {
                    Console.WriteLine($"    Conditionality: {entity.Assertion?.Conditionality}");
                }
            }
        }

        Console.WriteLine($"  We found {documentResult.EntityRelations.Count} relations in the current document:");
        Console.WriteLine();

        // View the healthcare entity relations that were recognized.
        foreach (HealthcareEntityRelation relation in documentResult.EntityRelations)
        {
            Console.WriteLine($"    Relation: {relation.RelationType}");
            if (relation.ConfidenceScore is not null)
            {
                Console.WriteLine($"    ConfidenceScore: {relation.ConfidenceScore}");
            }
            Console.WriteLine($"    For this relation there are {relation.Roles.Count} roles");

            // View the relation roles.
            foreach (HealthcareEntityRelationRole role in relation.Roles)
            {
                Console.WriteLine($"      Role Name: {role.Name}");

                Console.WriteLine($"      Associated Entity Text: {role.Entity.Text}");
                Console.WriteLine($"      Associated Entity Category: {role.Entity.Category}");
                Console.WriteLine();
            }

            Console.WriteLine();
        }

        Console.WriteLine();
    }
}

异步运行多个操作

此功能允许在一个或多个文档中运行多个操作。操作包括：

命名实体识别
PII 实体识别
链接实体识别
关键短语提取
情绪分析
医疗保健实体识别 (请参阅此处的示例)
自定义命名实体识别 (请参阅此处的示例)
自定义单标签分类 (请参阅此处的示例)
自定义多标签分类 (请参阅此处的示例)

    string documentA =
        "We love this trail and make the trip every year. The views are breathtaking and well worth the hike!"
        + " Yesterday was foggy though, so we missed the spectacular views. We tried again today and it was"
        + " amazing. Everyone in my family liked the trail although it was too challenging for the less"
        + " athletic among us.";

    string documentB =
        "Last week we stayed at Hotel Foo to celebrate our anniversary. The staff knew about our anniversary"
        + " so they helped me organize a little surprise for my partner. The room was clean and with the"
        + " decoration I requested. It was perfect!";

    // Prepare the input of the text analysis operation. You can add multiple documents to this list and
    // perform the same operation on all of them simultaneously.
    List<string> batchedDocuments = new()
    {
        documentA,
        documentB
    };

    TextAnalyticsActions actions = new()
    {
        ExtractKeyPhrasesActions = new List<ExtractKeyPhrasesAction>() { new ExtractKeyPhrasesAction() { ActionName = "ExtractKeyPhrasesSample" } },
        RecognizeEntitiesActions = new List<RecognizeEntitiesAction>() { new RecognizeEntitiesAction() { ActionName = "RecognizeEntitiesSample" } },
        DisplayName = "AnalyzeOperationSample"
    };

    // Perform the text analysis operation.
    AnalyzeActionsOperation operation = await client.AnalyzeActionsAsync(WaitUntil.Completed, batchedDocuments, actions);

    // View the operation status.
    Console.WriteLine($"Created On   : {operation.CreatedOn}");
    Console.WriteLine($"Expires On   : {operation.ExpiresOn}");
    Console.WriteLine($"Id           : {operation.Id}");
    Console.WriteLine($"Status       : {operation.Status}");
    Console.WriteLine($"Last Modified: {operation.LastModified}");
    Console.WriteLine();

    if (!string.IsNullOrEmpty(operation.DisplayName))
    {
        Console.WriteLine($"Display name: {operation.DisplayName}");
        Console.WriteLine();
    }

    Console.WriteLine($"Total actions: {operation.ActionsTotal}");
    Console.WriteLine($"  Succeeded actions: {operation.ActionsSucceeded}");
    Console.WriteLine($"  Failed actions: {operation.ActionsFailed}");
    Console.WriteLine($"  In progress actions: {operation.ActionsInProgress}");
    Console.WriteLine();

    await foreach (AnalyzeActionsResult documentsInPage in operation.Value)
    {
        IReadOnlyCollection<ExtractKeyPhrasesActionResult> keyPhrasesResults = documentsInPage.ExtractKeyPhrasesResults;
        IReadOnlyCollection<RecognizeEntitiesActionResult> entitiesResults = documentsInPage.RecognizeEntitiesResults;

        Console.WriteLine("Recognized Entities");
        int docNumber = 1;
        foreach (RecognizeEntitiesActionResult entitiesActionResults in entitiesResults)
        {
            Console.WriteLine($" Action name: {entitiesActionResults.ActionName}");
            Console.WriteLine();
            foreach (RecognizeEntitiesResult documentResult in entitiesActionResults.DocumentsResults)
            {
                Console.WriteLine($" Document #{docNumber++}");
                Console.WriteLine($"  Recognized {documentResult.Entities.Count} entities:");

                foreach (CategorizedEntity entity in documentResult.Entities)
                {
                    Console.WriteLine();
                    Console.WriteLine($"    Entity: {entity.Text}");
                    Console.WriteLine($"    Category: {entity.Category}");
                    Console.WriteLine($"    Offset: {entity.Offset}");
                    Console.WriteLine($"    Length: {entity.Length}");
                    Console.WriteLine($"    ConfidenceScore: {entity.ConfidenceScore}");
                    Console.WriteLine($"    SubCategory: {entity.SubCategory}");
                }
                Console.WriteLine();
            }
        }

        Console.WriteLine("Extracted Key Phrases");
        docNumber = 1;
        foreach (ExtractKeyPhrasesActionResult keyPhrasesActionResult in keyPhrasesResults)
        {
            Console.WriteLine($" Action name: {keyPhrasesActionResult.ActionName}");
            Console.WriteLine();
            foreach (ExtractKeyPhrasesResult documentResults in keyPhrasesActionResult.DocumentsResults)
            {
                Console.WriteLine($" Document #{docNumber++}");
                Console.WriteLine($"  Recognized the following {documentResults.KeyPhrases.Count} Keyphrases:");

                foreach (string keyphrase in documentResults.KeyPhrases)
                {
                    Console.WriteLine($"    {keyphrase}");
                }
                Console.WriteLine();
            }
        }
    }
}

疑难解答

常规

使用 .NET 文本分析 SDK 与语言认知服务交互时，语言服务返回的错误对应于为 REST API 请求返回的相同 HTTP 状态代码。

例如，如果提交一批包含重复文档 ID 的文本文档输入，则会返回错误 400 ，指示“请求错误”。

try
{
    DetectedLanguage result = client.DetectLanguage(document);
}
catch (RequestFailedException e)
{
    Console.WriteLine(e.ToString());
}

你会注意到会记录其他信息，例如操作的客户端请求 ID。

Message:
    Azure.RequestFailedException:
    Status: 400 (Bad Request)

Content:
    {"error":{"code":"InvalidRequest","innerError":{"code":"InvalidDocument","message":"Request contains duplicated Ids. Make sure each document has a unique Id."},"message":"Invalid document in request."}}

Headers:
    Transfer-Encoding: chunked
    x-aml-ta-request-id: 146ca04a-af54-43d4-9872-01a004bee5f8
    X-Content-Type-Options: nosniff
    x-envoy-upstream-service-time: 6
    apim-request-id: c650acda-2b59-4ff7-b96a-e316442ea01b
    Strict-Transport-Security: max-age=31536000; includeSubDomains; preload
    Date: Wed, 18 Dec 2019 16:24:52 GMT
    Content-Type: application/json; charset=utf-8

设置控制台日志记录

查看日志的最简单方法是启用控制台日志记录。若要创建将消息输出到控制台的 Azure SDK 日志侦听器，请使用 AzureEventSourceListener.CreateConsoleLogger 方法。

// Setup a listener to monitor logged events.
using AzureEventSourceListener listener = AzureEventSourceListener.CreateConsoleLogger();

若要详细了解其他日志记录机制，请参阅此处。

后续步骤

此 GitHub 存储库中提供了演示如何使用此客户端库的示例。为每个main功能区域提供示例，对于每个区域，提供示例用于分析单个文档，以及同步和异步模式下的文档集合。

高级示例

供稿

有关构建、测试和参与此库的详细信息，请参阅 CONTRIBUTING.md 。

本项目欢迎贡献和建议。大多数贡献要求你同意贡献者许可协议 (CLA)，并声明你有权（并且确实有权）授予我们使用你的贡献的权利。有关详细信息，请访问 cla.microsoft.com。

提交拉取请求时，CLA 机器人将自动确定你是否需要提供 CLA，并相应地修饰 PR（例如标签、注释）。直接按机器人提供的说明操作。只需使用 CLA 对所有存储库执行一次这样的操作。

此项目采用了 Microsoft 开放源代码行为准则。有关详细信息，请参阅行为准则常见问题解答，或如果有任何其他问题或意见，请与联系。

曝光数

适用于 .NET 的 Azure 认知服务文本分析客户端库 - 版本 5.3.0

入门

安装包

先决条件

创建认知服务资源或语言服务资源

验证客户端

获取 API 密钥

`TextAnalyticsClient`使用 API 密钥凭据创建

`TextAnalyticsClient`使用 Azure Active Directory 凭据创建

关键概念

`TextAnalyticsClient`

输入

对多个文档执行的操作

返回值

返回值集合

Long-Running操作

线程安全

其他概念

示例

同步示例

异步示例

检测语言

分析情绪

提取关键短语

识别命名实体

识别 PII 实体

识别链接实体

异步检测语言

异步识别命名实体

异步分析医疗保健实体

异步运行多个操作

疑难解答

常规

设置控制台日志记录

后续步骤

高级示例

供稿

反馈

其他资源

适用于 .NET 的 Azure 认知服务文本分析客户端库 - 版本 5.3.0

入门

安装包

先决条件

创建认知服务资源或语言服务资源

验证客户端

获取 API 密钥

TextAnalyticsClient使用 API 密钥凭据创建

TextAnalyticsClient使用 Azure Active Directory 凭据创建

关键概念

TextAnalyticsClient

输入

对多个文档执行的操作

返回值

返回值 集合

Long-Running操作

线程安全

其他概念

示例

同步示例

异步示例

检测语言

分析情绪

提取关键短语

识别命名实体

识别 PII 实体

识别链接实体

异步检测语言

异步识别命名实体

异步分析医疗保健实体

异步运行多个操作

疑难解答

常规

设置控制台日志记录

后续步骤

高级示例

供稿

反馈

其他资源

`TextAnalyticsClient`使用 API 密钥凭据创建

`TextAnalyticsClient`使用 Azure Active Directory 凭据创建

`TextAnalyticsClient`

返回值集合