Locally develop and manage LUIS applications with new BotBuilder tools

In our last article we introduced some of the helpful new tools from the Bot Builder tools suite. To recap, first we demonstrated how to create question/answer pairs using a new special markdown file format (.lu). Then, we used the new LUDown tool to parse the file to create a knowledge base (.json), and used the QnAMaker API tool to create….

Improving accuracy in LUIS with patterns

Since LUIS became generally available last year several new features have been added to make it easier to create and train robust natural language models for your applications. If you’ve used LUIS in the past, hopefully you’re pretty familiar with intents and entities, and how to train your model to use them by using utterances. In this post, we’ll go over….

Contributing to LUIS with Microsoft/Recognizers-Text – Part 3

Welcome to the final article of this series! Hopefully if you’re reading this, you know that this is article is part of an ongoing guide on how to extend the Microsoft/Recognizers-Text project to support new languages. This exciting new open-source project released by the LUIS team provides robust recognition and resolution for unit entities commonly expressed in everyday language. In our previous….

Contributing to LUIS with Microsoft/Recognizers-Text – Part 2

Previously in part 1, we gave an updated overview of the Recognizers-Text library which is used to power many of the prebuilt entities in LUIS. We provided a step-by-step guide to creating your own language specific definitions for both the .NET and Javascript versions of the project in YAML, and generating the platform-specific definitions using the tools already provided by the project.….

Contributing to LUIS with Microsoft/Recognizers-Text – Part 1

Last year, we announced a new recognizer library by the LUIS team, which provides robust recognition and resolution for common units expressed in everyday human interaction. Since then, the code base has changed considerably, and the library has been expanded to include more pre-built entities including date-time, currency, dimensions, and age. Today, we’ll take a look into the code base of Recognizers-Text,….

Updates for LUIS – January 2018

Some updates for new features to LUIS this month! DatetimeV2 prebuilt entity is now available for Brazilian Portuguese (pt-BR) Bing Spell Check has been upgraded to v7. If you’d like to continue using Bing Spell Check, you’ll need to get a v7 API key. Click here to learn how to enable Bing Spell Check in your LUIS applications If you have….

LUIS quick start with list entities

Since LUIS was first previewed, it’s certainly changed a lot! General availability for LUIS was announced back in December last month, with an expanded limit to 500 intents and 100 entities per application, support for new regions, availability in 7 new regions (South Central US, East US, West US 2, East Asia, North Europe, Brazil South, Australia East), as well as increased….

Conversational Bots Deep Dive – What’s new with the General Availability of Azure Bot Service and Language Understanding

Microsoft brings the latest advanced chatbot capabilities to developers’ fingertips, allowing them to create apps that see, hear, speak, understand, and interpret users’ needs — using natural communication styles and methods.” We’re excited to announce we’re making generally available Microsoft Cognitive Services Language Understanding service (LUIS) and Azure Bot Service, two top notch AI services to create digital agents that interact in natural ways and make sense….

Dialog management with QnA, LUIS, and Scorables

Figuring out how to manage your bot’s conversational flow is one of the most challenging aspects to bot development, and also related to some of the most commonly asked questions we receive from the community. In this article we’ll discuss different ways to manage your bot dialog by leveraging two popular Microsoft cognitive services – QnA Maker and LUIS. The sample….

Natural Language Understanding Services – a comparison

At this years’ SIGdial conference, researchers from the Technical University of Munich, department of informatics published the following paper – Evaluating Natural Language Understanding Services for Conversational Question Answering Systems Currently, there is no established way to evaluate different NLU (Natural Language Understanding) services. One of the primary goals of the research team was to define a way to compare these….