Language Understanding (LUIS)

A natural language understanding (NLU) AI service that allows users to interact with your applications, bots, and IoT devices by using natural language.

向应用添加自定义自然语言理解

Build applications that can understand natural language. Using machine teaching technology and our visual user interface, developers and subject matter experts can build custom machine-learned language models that interpret user goals and extract key information from conversational phrases—all without any machine learning experience.

使用开发人员工具和门户体验创建特定于用例的自定义语言模型,以简化标记

Build NLU models with no machine learning experience required

Run Language Understanding (LUIS) anywhere—in the cloud, on-premises, and at the edge with containers

信赖应用于数据和任何经训练的模型的企业级安全性和隐私

快速生成自定义语言解决方案

利用机器教学技术,你可以在没有标记数据的情况下开始工作,并以交互方式训练模型以加速开发。我们提供预构建的实体、功能和应用程序,以便快速启用你的项目。

阅读本入门指南

直观地教授语言模型

Teach language models like you would teach a person using machine teaching techniques. No machine learning expertise is needed. The simple, iterative process and visual interface make it simpler than ever before.

详细了解机器教学

始终学习并提高

Language Understanding (LUIS) enables developers to seamlessly improve language models over time based on real traffic.

详细了解主动学习

Build a comprehensive natural language understanding solution

Integrate seamlessly with Azure Cognitive Services like Text Analytics and Speech Services, as well as Azure Bot Services for an end-to-end conversational solution.

将语言理解添加到机器人

企业就绪,全球可用

Language Understanding (LUIS) scales to meet enterprise quality and performance needs, and meets international compliance standards including ISO, HIPAA, SOC, and FedRAMP.

See Language Understanding (LUIS) in action

智能光应用程序行为

LUIS 应用程序响应

Explore Language Understanding (LUIS) scenarios

生成企业级聊天机器人

生成企业级聊天机器人

This reference architecture describes how to build an enterprise-grade conversational bot (chatbot) using the Azure Bot Services framework.

商务聊天机器人

商务聊天机器人

Together, Azure Bot Services and Language Understanding (LUIS) enable developers to create conversational interfaces for various scenarios like banking, travel, and entertainment.

使用语音助理控制 IoT 设备

使用语音助理控制 IoT 设备

Create seamless conversational interfaces that understand natural language with all your internet-accessible devices—from your connected television or fridge to devices in a connected power plant.

行业领先的企业安全性

  • Microsoft 每年在网络安全研发方面的投资超过  USD 10 亿美元

  • 我们雇佣了超过 3,500 名安全专家专门负责数据安全和隐私方面的工作

  • Azure 拥有比任何其他云提供商都多的认证。查看完整列表

通过灵活的定价获取所需的功能、控制和自定义

Pay only for what you use, with no upfront costs. With Language Understanding (LUIS), pay as you go based on the number of transactions.

文档和资源

探索代码示例

查看自定义资源

Build natural language processing into apps, bots, and IoT devices with the LUIS portal.

See how trusted companies are applying Language Understanding (LUIS) models

Telefónica delivers an NLU- and AI-powered digital assistant

Telefónica 基于自然语言机器人构建了一个数字助手,可与客户进行更深层次的互动。

Telefonica

KPMG 为客户节省了数百万的合规费用

KPMG 在其客户风险分析解决方案中使用语言理解来提取信息并标记合规风险。

KPMG

Jet.com 能够用更快的速度为客户找到答案

Jet.com 利用认知服务为其客服聊天机器人注入智能,使其能够使用自然语言进行交流。

Jet.com

LaLiga 通过自己的虚拟助手提升参与度

"It's the easiest and most natural way for humans to interact, so we wanted to give our fans that option. They don't need to navigate through an app to find information, they just ask a question in plain language."

LaLiga 数字战略主管 Alfredo Bermejo
LaLiga

渐进式扩展 Flo 以更好地服务客户

"By using Microsoft Azure Bot Services and Cognitive Services… we've been able to continue our own Progressive journey of digital innovation and do it in an agile, fast, and cost-effective way."

Progressive Insurance 个人保险收益体验市场营销经理 Matt White
Progressive

Accenture 借助企业机器人成为新成员

"We're seeing great power in the solution we've developed with Bot Framework and Language Understanding. It's a huge 'aha' moment for us and for our HR leadership."

Accenture 高级架构师 Chellappan Murugappan
Accenture

Frequently asked questions about Language Understanding (LUIS)

  • 包含语言理解在内的 Azure 认知服务可确保 99.9% 的可用性。详细了解 SLA。
  • 可以。由于语言理解旨在针对你的方案进行定制,因此需要提供数据以训练模型。
  • 请参见语言可用性
  • 两者都是。使用站点访问用于训练模型的图形接口,在发布模型后,便可以调用服务进行预测。此外,还可以使用 SDK 训练模型。

准备好了就开始吧

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