Blogs by Ashwin

  Introduction You may process natural language for your own application by using the Azure AI Language service. One of the most prevalent AI issues is Natural Language Processing (NLP), which requires software to perceive voice or text in the same way that people do. Text classification is a component of Natural Language Processing (NLP), and Azure offers methods for text classification that include sentiment, language, and user-defined custom categories. Understand Types of Classification Projects Text files are given labels by custom text classification, which in the Azure AI Language service is a class that the developer defines. A video game synopsis, for instance, could be categorized as "Adventure," "Strategy," "Action," or "Sports." Custom text classification falls into two types of projects: Single Label Classification - you can assign only one class to each file. Following the above example, a video game summary could only be classifie...

  Use Patterns To Differentiate Similar Utterances A model may occasionally have several intents for which utterances are expected to be similar. While reducing the quantity of sample utterances, you can leverage the pattern of utterances to clarify the intents. Additionally, the intents could apply to a wide range of entity values. To correctly train your model, provide a handful of examples of each intent that specify the different formats of utterances. TurnOnDevice: "Turn on the {DeviceName}" "Switch on the {DeviceName}" "Turn the {DeviceName} on" GetDeviceStatus: "Is the {DeviceName} on[?]" TurnOffDevice: "Turn the {DeviceName} off" "Switch off the {DeviceName}" "Turn off the {DeviceName}" When you teach your model with each different type of utterance, the Azure AI Language service can learn how to categorize intents correctly based off format and punctuation. Use Pre-Built Entity Components To recognize common...

  Define Intents, Utterances, and Entities When a user interacts with an application that utilizes your language model, they may enter utterances. An intent, or more simply the meaning of an utterance, is a task or activity that the user want to carry out. By defining intents and linking them to one or more utterances, you can build a model. For example, consider the following list of intents and associated utterances: GetTime: "What time is it?" "What is the time?" "Tell me the time" GetWeather: "What is the weather forecast?" "Do I need an umbrella?" "Will it snow?" TurnOnDevice "Turn the light on." "Switch on the light." "Turn on the fan" None: "Hello" "Goodbye" Spend some time thinking about the domain your model must cover and the kinds of activities or information that users might request. This will help you establish the intentions that you want your model to comprehend....

  About You must construct a Language resource in Azure before you can utilize the Language Understanding service to create an NLP solution. This resource will be utilized for both creating your model and handling client application prediction queries. Build Your Model   Before employing a model to generate a prediction, you must develop, train, and implement it for features that need one. The Azure AI Language service will learn what to search for from this construction and training. In the Azure portal, you must first build your Azure AI Language resource. Then: Search for Azure AI services. Find and select Language Service. Select Create under the Language Service. Fill out the necessary details, choosing the region closest to you geographically (for best performance) and giving it a unique name. Once that resource has been created, you will need a key and the endpoint. You can find that on the left side under Keys and Endpoint of the resource overview page. Use Language St...

  Introduction One of Azure AI Language's primary custom features is CLU. In order to anticipate general intent and extract crucial information from incoming utterances, CLU assists users in creating unique natural language understanding models. To educate CLU how to reliably predict entities and intentions, the user must tag the data. Custom Named Entity Recognition Custom entity recognition takes custom labeled data and extracts specified entities from unstructured text. For example, if you have various contract documents that you want to extract involved parties from, you can train a model to recognize how to predict them. Custom Text Classification Custom text classification enables users to classify text or documents as custom defined groups. For example, you can train a model to look at news articles and identify the category they should fall into, such as News or Entertainment. Question Answering Question answering is a largely pre-configured function that responds to inputt...

  Pre-Configured Features The Azure AI Language service offers specific functionalities without requiring any model labelling or training. After you set up your resource, you can transmit your data and utilize the results returned in your application. The following features are all pre-configured: Summarization Summarization is available for both documents and conversations, and will summarize the text into key sentences that are predicted to encapsulate the input's meaning. Named Entity Recognition Entity recognition can identify and extract various entities like individuals, locations, or organizations, enhancing your application's ability to understand different entity types for better natural language interaction. For instance, in the sentence "The waterfront pier is my favorite Seattle attraction," Seattle would be recognized and classified as a location. Personally Identifiable Information (PII) Detection PII detection allows you to identify, categorize, and red...