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Revision as of 10:02, 23 September 2020

Getting Started
Grooper is a software application that helps organizations innovate workflows by integrating difficult data. Grooper empowers rapid innovation for organizations processing and integrating large quantities of difficult data. Created by a team of courageous developers frustrated by limitations in existing solutions, Grooper is an intelligent document and digital data integration platform. Grooper combines patented and sophisticated image processing, capture technology, machine learning, and natural language processing. Grooper – intelligent document processing; limitless, template-free data integration.	Getting Started
	Install and Setup
	2.90 Reference Documentation

Featured Articles

Did you know?

Data Context

Data without context is meaningless. Context is critical to understanding and modeling the relationships between pieces of information on a document. Without context, it’s impossible to distinguish one data element from another. Context helps us understand what data refers to or “means”.

This allows us to build an extraction logic using Data Type and Field Class extractors in order to build and populate a Data Model.

There are three fundamental data context relationships:

Syntactic - Context given by the syntax of data.
Semantic - Context given by the lexical content associated with the data.
Spatial - Context given by where the data exists on the page, in relationship with other data.

Using the context these relationships provide allows us to understand how to target data with extractors.

You can now manually manipulate the confidence of an extraction result. The Confidence Multiplier and Output Confidence properties of Data Type and Data Format extractors allow you to change the confidence score of extraction results. No longer are you forced to accept the score Grooper provides. These properties give you more control when it comes to what confidence a result should be.

This allows you to prioritize certain results over others. You can create a kind of "fall back" or "safety net" result by using this property. You can even increase the confidence of an extractor's result, allowing you to give more weight to a fuzzy extractor's result over a non-fuzzy one, for example.

For more information visit, the Confidence Multiplier and Output Confidence article.

New in 2.9

Featured Use Case

Welcome to Grooper 2.9! Below you will find helpful links to all the articles about the new/changed functionality in this version of Grooper.

Compile Stats	Microsoft Office Integration	Document Viewer	Separation and Separation Review

Data Review	Confidence Multiplier	Data Element Overrides	Database Export

CMIS Lookup	Content Type Filter	Output Extractor Key	Box (CMIS Binding)

LINQ to Grooper Objects

They’re Saving Over 5,000 Hours Every Year in Data Discovery and Processing

American Airlines Credit Union has transformed their data workflows, quickly saving thousands of hours in electronic data discovery , resulting in much greater efficiency and improved member services.

Discover how they:

Quickly found 40,000 specific files among one billion
Easily integrated with data silos and content management systems when no other solution would
Have cut their mortgage processing time in half (and they process mortgages for 47 branch offices!)
Learn from the document and electronic data discovery experts at BIS!

You can access the full case study clicking this link.

Other Resources
Grooper x Change Grooper Downloads - Including Grooper installer files Grooper.com	2.80 Reference Documentation 2.80 SDK Documentation 2.90 Reference Documentation	Webinars and Video BIS White Papers Case Studies BIS Blog	ACE Training Schedule

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-[[File:weighted_rules_00.png|right|250px|link=Confidence Multiplier]]
 <blockquote style="font-size:14pt">
-'''[[Confidence Multiplier]]'''
+[[Data Context]]
 </blockquote>
-The '''''Confidence Multiplier''''' property of a '''[[Data Type]]''' allows you to manually alter the confidence score of returned values.
-Use of this property is sometimes referred to as ''weighted rules''.  Its practical application allows a user to increase or decrease set the of a '''Data Type''' extractor's result (or set its confidence to an assigned value).  This changes the confidence of the extractor's results, making them appear more (or less) favorable.  When used in combination with the '''''Order By''''' property set to ''Confidence'' on a parent '''Data Type''', you can manipulate which result the parent prioritizes.
+Data without context is meaningless.  Context is critical to understanding and modeling the relationships between pieces of information on a document.  Without context, it’s impossible to distinguish one data element from another.  Context helps us understand what data refers to or “means”.
-In effect, you change the ''rules'' about the ''weight'' or priority a particular child extractor by changing its confidence scores.
+This allows us to build an extraction logic using '''[[Data Type]]''' and '''[[Field Class]]''' extractors in order to build and populate a '''[[Data Model]]'''.
-===General Usage===
+There are three fundamental data context relationships:
-Modifying the '''Confidence Multiplier''' property of a '''Data Type'''  is done by clicking on the ellipses in the '''''Result Options''''' property which opens the '''''Result Options''''' submenu.
-The '''''Confidence Multiplier''''' property defaults to ''1'' and can be changed in this submenu. The field is a double and takes floating point values.
+* '''Syntactic''' - Context given by the syntax of data.
+* '''Semantic''' - Context given by the lexical content associated with the data.
+* '''Spatial''' - Context given by where the data exists on the page, in relationship with other data.
-For example, a value of ''0.5'' will multiply the confidence of output results by 0.5. If the output confidence was 100%, now it will be 50%. Similarly, you can increase the confidence, even above 100%. If the '''''Confidence Multiplier''''' property is set to ''3'', and an output result had a 50% confidence, it will not display as 150% confidence.
+Using the context these relationships provide allows us to understand how to target data with extractors.
 |
-You can talk to us!
+You can now manually manipulate the confidence of an extraction result.  The '''''[[Confidence Multiplier and Output Confidence]]''''' properties of '''[[Data Type]]''' and '''[[Data Format]]''' extractors allow you to change the confidence score of extraction results.  No longer are you forced to accept the score Grooper provides.  These properties give you more control when it comes to what confidence a result ''should'' be.
-Do you have an idea for an article?  Have you noticed something missing from one already in the wiki?  Do you have other comments or feedback about the wiki?
+This allows you to prioritize certain results over others.  You can create a kind of "fall back" or "safety net" result by using this property.  You can even ''increase'' the confidence of an extractor's result, allowing you to give more weight to a fuzzy extractor's result over a non-fuzzy one, for example.
-If so, check out the [https://xchange.grooper.com/categories/documentation-requests Documentation Requests] section of Grooper x Change.  This is your way to communicate with our documentation team.  Please, let us know how we can continue to improve our wiki.
+For more information visit, the [[Confidence Multiplier and Output Confidence]] article.
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+* [https://grooper.bisok.com/Documentation/2.90/Main/HTML5/index.htm#t=Start_Page.htm 2.90 Reference Documentation]
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 * [https://blog.bisok.com/webinars Webinars and Video]