What is Entity Extraction and How Does it Work?

Rachelle Graham

Jan 2

Nowadays, several companies offer fast and more accurate entity extraction in several languages using advanced technologies like machine learning and AI-based natural language processing. You can use multiple tools to perform data validation and extraction to provide customer service. Let's see how these tools work and how they can be used in your environment.

Suppose you have an invoice with the purchase details of a particular service. It contains information like supplier name and invoice number but also several fields that are not part of the invoice template: customer account, customer name, etc. The invoice is a single document aggregating multiple sales invoices, so you can easily merge them into a single document containing all the necessary information.

The Process is As Simple As Using Mint (the Automated Workflow Manager) to Create a New Document with All These Additional Fields, Which Then Get Added to the Original Documents in One Step:

If you want to extract these additional fields from your document and put them into a new document, you can use Mint. It's an automated workflow manager that allows users to create workflows to automate repetitive tasks.

To Do Extraction in Mint:

Create a new document for extracting entities (it is nothing but "Extracted Invoice") using the same layout as the original invoice template;
In the "Document" tab, select all columns containing data that needs processing and click the "Processing" button;
In the Settings view, select the "Entity Extraction" option under the Processing Actions heading (top right corner);
Select which fields should be extracted and add them as sub-entities under each field name;
Click Apply Changes button at the bottom of the window.

This process works well for some companies but could be more efficient for others. The key is to audit your templates and modify them according to your needs:

Suppose your customer data is stored in a spreadsheet. You can easily create a new template that extracts the customer data from the original document and then stores it in another location, such as Salesforce or SalesLoft. The benefit of this approach is that it allows you to work with multiple data sources simultaneously, which would otherwise have been easier if you were working with only one source at a time.
One way to handle this situation is by creating an intelligent rule-based system that automatically copies over any relevant fields for each record as it's added to your system. The drawback here is that if there are duplicate lines in one file—for example, if someone added "John" twice—then those will be copied over twice instead of once per record.

Entity AI Extraction is a Powerful Tool That Can Make Your Life Easier:

In this section, let's discuss how to extract these extra fields from your entity data using Mint or Workflow Manager.

Extracting Data From the Customer Entity:

To extract the extra fields from the customer entity, you must create a new Mint workflow and add it to your system. In this example, you will use a new workflow that extracts all attributes from the Customer entity.

Create a New Workflow with Mint:

If you already have an existing workflow in Mint, you should create a new one instead of deleting your existing one and keeping only one copy for this example. In this way, each time you want to work on extracting extra fields from customers' activity records, you can use your new customized workflow rather than using the default Workflow Manager method.

Conclusion:

In a professional tone, SQL Server can provide a data warehouse that multiple applications can access.

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