Contents
How does entity extraction work?
Entity extraction is a text analysis technique that uses Natural Language Processing (NLP) to automatically pull out specific data from unstructured text, and classifies it according to predefined categories. These categories are named entities, the words or phrases that represent a noun.
What is entity extraction model?
The prebuilt entity extraction model recognizes specific data from text that’s of interest to your business. The model identifies key elements from text, and then classifies them into predefined categories. This can help to transform unstructured data into structured data that’s machine-readable.
How to create a custom Entity Extraction Model?
To create your custom entity extraction model: Provide at least 10 examples of your text data. Review the results from existing, prebuilt entities. Refine your results by creating your own custom tables or modifying existing, prebuilt tables. Review your model, and train it. Evaluate your model (optional).
What can entity extraction ( EE ) be used for?
Entity Extraction (EE) is also useful for parsing structured documents like forms, W4s, receipts, business cards, and restaurant menus (which is what we’ll be using it for today).
Make sure you use a comma as the column separator. If the file contains non-ASCII characters such as diacritics, you must encode it in UTF-8. Save the file to a location that is accessible from the server from which you will run the Microsoft PowerShell cmdlet to deploy the custom entity extraction dictionary.
How to use custom entities in classic search?
To use custom entities as refiners in classic search, you first create a custom entity extraction dictionary and deploy it. Then, you configure a managed property to use a custom entity extractor and run a full crawl. After that, you can configure the Refinement Web Part on the search results page to use the custom entity as a refiner.