Blogs by Ashwin

  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...

  Introduction You may develop a model that apps can use to extract meaning from natural language using the Azure AI Language Conversational Language Understanding service (CLU). Software must be able to handle text or speech in the natural language format that a human user would write or speak. This is known as Natural Language Processing, or NLP. Natural Language Understanding (NLU), a subset of Natural Language Processing (NLP), addresses the challenge of deriving semantic meaning from natural language, typically through the use of a trained language model. Azure AI Language enables developers to build apps based on language models that can be trained with a relatively small number of samples to discern a user's intended meaning. Understand Prebuilt Capabilities of the Azure AI Language Service The Azure AI Language service offers a number of tools for comprehending human language. You can use each feature to better communicate with users, better understand incoming communicatio...

  Improve Question Answering Performance You can enhance a knowledge base's performance by defining synonyms and using active learning after it has been created and tested. Use Active Learning Over time, active learning can assist you in improving your ability to accurately respond to consumer inquiries. People frequently pose questions with similar meanings but different wording. Because it allows you to think of other questions for each question and answer pair, active learning can be useful in circumstances such as these. By default, active learning is enabled. To use active learning, you can do the following: Create Your Question and Answer Pairs For your project, you use Language Studio to build pairs of questions and answers. To upload questions and answers in bulk, you may alternatively import a file. Review Suggestions After then, active learning starts to provide different questions for every question in your pairs of questions and answers. From the Review recommendations ...

  Create a Knowledge Base You can write code that defines, trains, and publishes the knowledge base using the REST API or SDK to create a question-answering solution. However, defining and managing a knowledge base is most frequently done through the Language Studio web interface. To create a knowledge base you: Sign in to Azure portal. Search for Azure AI services using the search field at the top of the portal. Select Create under the Language Service resource. Create a resource in your Azure subscription- Enable the question answering feature. Create or select an Azure AI Search resource to host the knowledge base index. In Create or select an Azure AI Search resource to host the knowledge base index. Language Studio, select your Azure AI Language resource and create a Custom question answering project. Add one or more data sources to populate the knowledge base- URLs for web pages containing FAQs. Files containing structured text from which questions and answers can b...

  Introduction The Azure AI Language service's question-answering feature makes it simple to create applications where users can ask questions in natural language and get relevant responses. Enabling users to ask inquiries in natural language and obtain pertinent responses is a typical pattern for "intelligent" products. Conversational intelligence is effectively added to a conventional frequently asked questions (FAQ) publication with this type of solution. Understand Question Answering You may create a knowledge base of question and answer pairs that can be queried using natural language input thanks to Azure AI Language's question answering feature. Client applications, typically bots, can access the knowledge base by publishing it to a REST API. The knowledge base can be created from existing sources, including: Web sites containing frequently asked question (FAQ) documentation. Files containing structured text, such as brochures or user guides. Built-in...