2023.1:Content Model (Node Type)

This article is about an older version of Grooper. Information may be out of date and UI elements may have changed.
2025	2023.1

stacks Content Model nodes define a classification taxonomy for document sets in Grooper. This taxonomy is defined by the collections_bookmark Content Categories and description Document Types they contain. Content Models serve as the root of a Content Type hierarchy, which defines Data Element inheritance and Behavior inheritance. Content Models are crucial for organizing documents for data extraction and more.

Glossary

Batch: inventory_2 Batch nodes are fundamental in Grooper's architecture. They are containers of documents that are moved through workflow mechanisms called settings Batch Processes. Documents and their pages are represented in Batches by a hierarchy of folder Batch Folders and contract Batch Pages.

Behavior: A "Behavior" is one of several features applied to a Content Type (such as a description Document Type). Behaviors affect how certain Activities and Commands are executed, based how a document (folder Batch Folder) is classified. They behave differently, according to their Document Type. This includes how they are exported (how Export behaves), if and how they are added to a document search index (how the various indexing commands behave), and if and how Label Sets are used (how Classify and Extract behave in the presence of Label Sets).

• Each Behavior is enabled by adding it to a Content Type. They are configured in the Behaviors editor.
• Behaviors extend to descendent Content Types, if the descendent Content Types has no Behavior configuration of its own.
  • For example, all Document Types will inherit their parent Content Model's Behaviors.
  • However, if a Document Type has its own Behavior configuration, it will be used instead.

Classification Method:

Classification: Classification is the process of identifying and organizing documents into categorical types based on their content or layout. Classification is key for efficient document management and data extraction workflows. Grooper has different methods for classifying documents. These include methods that use machine learning and text pattern recognition. In a Grooper Batch Process, the Classify Activity will assign a Content Type to a folder Batch Folder.

Content Category: collections_bookmark A Content Category is a container for other Content Category or description Document Type nodes in a stacks Content Model. Content Categories are often used simply as organizational buckets for Content Models with large numbers of Document Types. However, Content Categories are also necessary to create branches in a Content Model's classification taxonomy, allowing for more complex Data Element inheritance and Behavior inheritance.

Content Model: stacks Content Model nodes define a classification taxonomy for document sets in Grooper. This taxonomy is defined by the collections_bookmark Content Categories and description Document Types they contain. Content Models serve as the root of a Content Type hierarchy, which defines Data Element inheritance and Behavior inheritance. Content Models are crucial for organizing documents for data extraction and more.

Content Type: Content Types are a class of node types used used to classify folder Batch Folders. They represent categories of documents (stacks Content Models and collections_bookmark Content Categories) or distinct types of documents (description Document Types). Content Types serve an important role in defining Data Elements and Behaviors that apply to a document.

Data Element: Data Elements are a class of node types used to collect data from a document. These include: data_table Data Models, insert_page_break Data Sections, variables Data Fields, table Data Tables, and view_column Data Columns.

Data Extraction: Data Extraction involves identifying and capturing specific information from documents (represented by folder Batch Folders in Grooper). Extraction is performed by configurable Data Extractors, which transform unstructured or semi-structured data into a structured, usable format for processing and analysis.

Data Model: data_table Data Models are leveraged during the Extract activity to collect data from documents (folder Batch Folders). Data Models are the root of a Data Element hierarchy. The Data Model and its child Data Elements define a schema for data present on a document. The Data Model's configuration (and its child Data Elements' configuration) define data extraction logic and settings for how data is reviewed in a Data Viewer.

Document Type: description Document Type nodes represent a distinct type of document, such as an invoice or a contract. Document Types are created as child nodes of a stacks Content Model or a collections_bookmark Content Category. They serve three primary purposes:

1. They are used to classify documents. Documents are considered "classified" when the folder Batch Folder is assigned a Content Type (most typically, a Document Type).
2. The Document Type's data_table Data Model defines the Data Elements extracted by the Extract activity (including any Data Elements inherited from parent Content Types).
3. The Document Type defines all "Behaviors" that apply (whether from the Document Type's Behavior settings or those inherited from a parent Content Type).

ESP Auto Separation: ESP Auto Separation is a Separation Provider used for document separation. It is unique in that it both separates and classifies documents at the same time. It uses page-level classification training examples (among other things) to determine where to insert document folders in a inventory_2 Batch.

Extract: export_notes Extract is an Activity that retrieves information from folder Batch Folder documents, as defined by Data Elements in a data_table Data Model. This is how Grooper locates unstructured data on your documents and collects it in a structured, usable format.

GPT Embeddings: BE AWARE: GPT Embeddings is obsolete as of version 2025. The LLM Classifier and Search Classifier methods are the new and improved AI-enabled classification methods. GPT Embeddings is a Classify Method that uses an OpenAI embeddings model and trained document samples to tell one document from another.

Labelset-Based: "Labelset-Based" is a Classify Method that leverages the labels defined via a Labeling Behavior to classify folder Batch Folders.

Lexical: "Lexical" is a Classify Method that classifies folder Batch Folders based on the text content of trained document examples. This is achieved through the statistical analysis of word frequencies that identify description Document Types.

Node Tree: The Node Tree is the hierarchical list of Grooper node objects found in the left panel in the Design Page. It is the basis for navigation and creation in the Design Page.

Project: package_2 Projects are the primary containers for configuration nodes within Grooper. The Project is where various processing objects such as stacks Content Models, settings Batch Processes, profile objects are stored. This makes resources easier to manage, easier to save, and simplifies how node references are made in a Grooper Repository.

Rules-Based: "Rules-Based" is a Classify Method that employs "rules" defined on each description Document Type to classify folder Batch Folders. Positive Extractor and Negative Extractor properties are configured for each Document Type to positively or negatively associate a Batch Folder based on predefined criteria.

• Where the Positive and Negative Extractors will impact all Classify Method results, the Rules-Based method classifies using only these properties and nothing else.

Separation: Separation is the process of taking an unorganized inventory_2 Batch of loose contract Batch Pages and organizing them into documents represented by folder Batch Folders in Grooper. This is done so Grooper can later assign a description Document Type to each document folder in a process known as "classification".

Visual: "Visual" is a Classify Method that uses image analysis instead of text data to determine the description Document Type assigned to a folder Batch Folder during classification. Instead of using text-based extractors, an "Extract Features" IP Command in an perm_media IP Profile is used to collect image-based data from a Batch Folder's image(s). This image-based data is compared against that of previously trained document examples of each Document Type to classify the Batch Folder.

About

A Content Model is the digital representation in Grooper of a document set's content. What content you want to glean from your documents is all set up within a Content Model, including the system for classifying documents and what data you want to extract from them.

Content Models are the fundamental Content Type.  Other Content Types, such as Document Types, are established within a Content Model.  Content Models have two main purposes in Grooper:  

• Document Classification
• Data Extraction

You may download the ZIP(s) below and upload it into your own Grooper environment (version 2023). The first contains one or more Batches of sample documents. The second contains one or more Projects with resources used in examples throughout this article. Media:2023.1_Wiki_Content-Model_Batch.zip Media:2023.1_Wiki_Content-Model_Project.zip

Uses of a Content Model

Let's look at how Document Classification and Data Extraction can be used on a Content Model:

Document ClassificationData Extraction

Document Classification is an important task that the Content Model helps facilitate.

1. The very first property of a Content Model is the Classification Method. This tells Grooper how to classify documents.
2. This can be done one of five ways:
   • GPT Embeddings
   • Labelset-Based
   • Lexical
   • Rules-Based
   • Visual

1. Another important tenet of classification that is relied upon by both Grooper and the Content Model is the Document Type. This is a child object of the Content Model that is used to identify certain documents through positive extractors.
   • For more information on Document Types, click here
2. For Documents that are more difficult for Grooper to classify, or if you don't want to set up a Classification Method, you can set a Default Content Type.
   • This is optional. If you have multiple Document Types, it's best just to set a Classification Method on the Content Model.
3. You will need to have a Document Type created in order to do so.

FYI	GPT Embeddings is a fairly new Classification Method and is still currently in beta.

Data Extraction is another important job for a Content Model. This tells Grooper what you want done with the data from your documents, where you want it to go, and how you want it handled.

1. Select the Behaviors property.
2. This will open the List of Behaviors window.
   • See here for more information on Behaviors
3. To add Behaviors, press the Add button.
4. From the drop-down list, select your desired Behavior, based upon how you want to extract your data.

Brief Note on Document Types

Document Types are child objects of a Content Model. One cannot classify without a Document Type. The Classification Method on a Content Model may tell Grooper how to classify, but the Document Type tells Grooper what label to slap on the document.

1. To add a Document Type, right-click the Content Model in the Node Tree.
2. Select Add, then Document Type.

1. This will create the Document Type.
2. Extractors are a property that Grooper uses to help in identifying and classifying documents as different Document Types.
   • Positive Extractors tell Grooper what to look for.
     • In short, wherever the Positive Extractor extracts a piece of data that Grooper is told to look for, then the document is classified as whatever document type has been configured. This is a good tool to use whenever you have documents that are similar to one another, where classification could go awry.
   • Similarly, Negative Extractors tell Grooper what to exclude from being classified as a potential Document Type.
3. These Separation properties on the Document Type are only for ESP Auto Separation. ESP Auto Separation is a type of Separation Provider that both separates and classifies documents.

Wrap-Up

Content Models define the classification taxonomy for a set of documents.  This means a list of distinct types of documents (via Document Types), their hierarchical structure within the Content Model (via optional Content Categories). How a document is classified is defined here as well (via the Classification Method and the Document Types).  

Hand-in-hand with the classification taxonomy, Content Models also define the hierarchical data structure for the documents and document set (via Data Models of the various Content Types in the Content Model). The Data Models and their Data Elements define what data is extracted from documents and how that is accomplished.