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Tokenizers

By Ashwin Venugopal -
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  What are tokenizers ? The part that separates the text into the tokens that will be kept in the index is called the tokenizer. Additionally, tokenizers deconstruct words into their most basic forms. Often, a token is a single word, but you might want to create unusual tokens such as: A full postal address. A complete URL or email address. Words based on the grammar of a specific language. There are 13 different tokenizers to choose from. These tokenizers include: classic- This tokenizer processes text based on grammar for European languages. keyword- This tokenizer emits the entire input as a single token. Use this tokenizer for fields that should always be indexed as one value. lowercase- This tokenizer divides text at non-letters and then modifies the resulting tokens to all lower case. microsoft_language_tokenizer - This tokenizer divides text based on the grammar of the language you specify. pattern- This tokenizer divides texts where it matches a regular expression that y...
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Analyzers and Tokenizers

By Ashwin Venugopal -
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  Improve an Index With Analyzers and Tokenized Terms By default, Azure AI Search is set up to examine text and find useful tokens for your index. The appropriate tokens guaranty that users may swiftly locate the documents they require. An ideal index is often produced by the default configuration. However, you might wish to specify exactly how text is processed if you have uncommon or unique fields. Analyzers in AI Search Text is retrieved by AI Search when it indexes your material. That text must be processed in order to create a helpful index with terms that aid users in finding documents. For example: The text should be broken into words, often by using whitespace and punctuation characters as delimiters. Stopwords, such as "the" and "it", should be removed because users don't search for them. Words should be reduced to their root form. For example, past tense words, such as "ran", should be replaced with present tense words, such as "run...
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Improve Relevance Of Results By Adding Scoring Profiles

By Ashwin Venugopal -
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  How Search Scores Are Calculated ? The final stage of processing a search query is scoring. The documents that are retrieved from the first three stages are ranked by the search engine. The number of times detected search phrases exist in a document, the size of the document, and the rarity of each keyword all affect the score. The search results are automatically sorted by search score, with the highest score appearing first. An $orderby clause can be included to break a tie between two documents that have the same search score. Improve Score For More Relevant Documents The final score may not be the highest for the most relevant document because the default scoring is based on phrase frequency and rarity. Because every dataset is unique, AI Search allows you to use scoring profiles to affect a document's score. The most straightforward scoring profile defines different weights for fields in an index. Functions like freshness and distance can also be included in the score profil...
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Implement Advanced Search Features In Azure AI Search

By Ashwin Venugopal -
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  Introduction Azure AI Search is a robust search engine that can index a large amount of data from several sources. Returning pertinent results from search queries is a fundamental aspect of search. Improve Ranking Of a Document With Term Boosting When the most pertinent results are displayed first, search performs best. Every search engine aims to provide the most pertinent answers to inquiries. For full text search, Azure AI Search uses an improved version of Apache Lucene. Search an Index With Azure AI Search, you may use the search explorer tool within the Azure portal or a REST endpoint to query an index. You will use the search explorer to see the difference between using the simple and full query type changes your search results. Improve Relevance Of Results By Adding Scoring Profiles Azure AI Search uses the BM25 similarity ranking algorithm. The algorithm scores documents based on the search terms used. How Search Scores Are Calculated Scoring is the last phase of process...
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Conversation Language Understanding Model (Part 3)

By Ashwin Venugopal -
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  Train Your Classification Model It requires recognized data to be trained, just like any other AI model. In order for the model to be tested, it must see examples of how to map data to a class. You have the option to allow the model to automatically divide your training data; by default, it will utilize 20% of the documents for blind testing and 80% of the documents for training. You can designate certain documents for testing if you want to use them to test your model. Choose Data labeling for your project in Language Studio. All of your documents will be visible to you. After choosing each document to include in the testing set, click Testing the model's performance. Create a new training job after saving your amended labels. Create Search Index Creating a search index that will be enhanced by a custom text classification model doesn't require any special actions on your part. Once you have developed a function app, you will be changing the index, indexer, and custom skill....
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Conversation Language Understanding Model (Part 2)

By Ashwin Venugopal -
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  Store Your Data Language Studio and Azure AI Services both provide access to Azure Blob storage. The easiest option is to select Container as the container must be available, although private containers can also be used with some extra setup. You also need a method for classifying each document in addition to your data. You can manually categorize each document one at a time using the graphical interface that Language Studio offers. There are two types of projects available, if a document belongs to a single class, utilize a single label categorization project. Use the multi-label categorization project if you are able to map a document to multiple classes. If you don't want to manually classify each document, you can label all your documents before you create your Azure AI Language project. Create Your Azure AI Language Project Your Azure AI Language project can be created in one of two ways. Language Studio will offer to build a language service for you if you begin using it wi...
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Conversation Language Understanding Model (Part 1)

By Ashwin Venugopal -
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  Create, Train, and Deploy Conversation Language Understanding Model Each customizable feature within Azure AI Language requires different procedures to create the models. The goal of conversational language understanding is to develop a model that can predict intentions based on conversational input. For instance, consider an email application that you can interact with through chat to send messages or flag items. You would train the model using phrases such as "please flag that email" or "I’m ready to send it." These phrases would be converted to intents for flag email and send email To use language understanding you'll need to have an Azure AI Language resource already created in Azure, then you can carry out the following steps in Language Studio: From the home page, you select Conversational Language Understanding in the Understand questions and conversational language tab. Select + Create new project. Enter a name for the new project. Select your language...
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