Extractor Node

About

Types of Extractor Nodes

There are three types of Extractor Nodes in Grooper:

quick_reference_all Value Reader

pin Data Type

input Field Class

• Advances in large-language models (LLMs) have largely made Field Classes obsolete. LLM-based extraction methods in Grooper (such as AI Extract) can achieve similar results with nowhere near the amount of set up.

All three of these node types perform a similar function. They return data from documents. However, they differ in their configuration and utility.

Extractor Nodes are tools to extract/return document data. But they don't do anything by themselves. They are used by extractor properties on other nodes in Grooper.

• Example: Data Elements, such as Data Fields, are ultimately collect document data. It is the Data Field's "Value Extractor" configuraiton that does this. However, you may configure a Data Field's Value Extractor with an Extractor Node to do so.
• Example: The Pattern-Based Separation provider's "Value Extractor" is ultimately what identifies patterns to separate Batch Pages into Batch Folders. However, you may configure the Value Extractor with an Extractor Node to do so.
• Example: When Classify runs on a document, a Document Type's "Positive Extractor" will be used to assign a Batch Folder the Document Type if it returns a value. You may configure the Positive Extractor with an Extractor Node to do so.
• And so on and so on for any extractor property for any node in Grooper.

To that end, Extractor Nodes serve three purposes:

1. To be re-usable units of extraction
2. To collate data
3. To leverage machine learning algorithms to target data in the flow of text
   • Advances in large-language models (LLMs) have largely made Field Classes obsolete. LLM-based extraction methods in Grooper (such as AI Extract) can achieve similar results with nowhere near the amount of set up.

Re-usability

Extractor nodes are meant to be referenced either by other extractor nodes or, importantly, by Data Elements such as Data Fields in a Data Model.

For example, an individual Data Field can be configured on its own to collect a date value, such as the "Received Date" on an invoice. However, what if another Data Field is collecting a different date format, like the "Due Date" on the same invoice? In this case you would create one extractor node, like a Value Reader, to collect any and all date formats. You could then have each Data Field reference that single Value Reader and further configure each individual Data Field to differentiate their specific date value.

Data collation

Another example would be configuring a Data Type to target entire rows of information within a table of data. Several Value Reader nodes could be made as children of the Data Type, each targeting a specific value within the table row. The parent Data Type would then collate the results of its child Value Reader nodes into one result. A Data Table would then reference the Data Type to collect the appropriate rows of information.

Machine learning

Many documents contain important pieces of information buried within the flow of text, like a legal document. These types of documents and the data they contain require an entirely different approach to extracting data than a highly structured document like an invoice. For these situations you can use a "trainable" extractor known as a Field Class to leverage machine learning algorithms to target important information.

• Advances in large-language models (LLMs) have largely made Field Classes obsolete. LLM-based extraction methods in Grooper (such as AI Extract) can achieve similar results with nowhere near the amount of set up.

Extractor Nodes vs Value Extractors

Extractor nodes should not be confused with "Value Extractors". There are many places in Grooper where extraction logic can be applied for one purpose or another. In these cases a Value Extractor is chosen to define the logic required to return a desired value.

In fact, the Extractor Nodes themselves will leverage specific Value Extractors to define their logic.

• Example: "Value Readers" are configured using a single property "Extractor". This property specifies a single Value Extractor which determines how data is extracted. Value Readers are essentially an encapsulation of a single Value Extractor configuration that can be reused by multiple other extraction elements and properties, such as Data Fields and Data Types.
• Example: "Data Types" have several properties that can be configured with Value Extractors, including its "Local Extractor", "Input Filter", and "Exclusion Extractor" properties.
• Example" "Field Classes" cannot function without its "Value Extractor" and "Feature Extractor" properties configured, both of which specify a Value Extractor.

However, Extractor Nodes are used when you need to reference them for their designated strengths:

• re-usability
• collation
• machine learning
  • Advances in large-language models (LLMs) have largely made Field Classes obsolete. LLM-based extraction methods in Grooper (such as AI Extract) can achieve similar results with nowhere near the amount of set up.

Related Node Types

Value Reader

quick_reference_all Value Reader nodes define a single data extraction operation. Each Value Reader executes a single Value Extractor configuration. The Value Extractor determines the logic for returning data from a text-based document or page. (Example: Pattern Match is a Value Extractor that returns data using regular expressions).

• Value Readers are can be used on their own or in conjunction with pin Data Types for more complex data extraction and collation.

Data Type

pin Data Types are nodes used to extract text data from a document. Data Types have more capabilities than quick_reference_all Value Readers. Data Types can collect results from multiple extractor sources, including a locally defined extractor, child extractor nodes, and referenced extractor nodes. Data Types can also collate results using Collation Providers to combine, sift and manipulate results further.

• For example, if you're extracting a date that could appear in multiple formats within a document, you'd use various extractor nodes (each capturing a different format) as children of a Data Type.

The Data Type also defines how to collate results from one or more extractors into a referenceable output. The simplest type of collation (Individual) would just return all individual extractors' results as a list of results.

Data Types are also used for recognizing complex 2D data structures, like address blocks or table rows. Different collation methods would be used in these cases to combine results in different ways.

Field Class

input Field Classes are NLP (natural language processing) based extractor nodes. They find values based on some natural language context near that value. Values are positively or negatively associated with text-based "features" nearby by training the extractor. During extraction, the extractor collects values based on these training weightings.

• Field Classes are most useful when attempting to find values within the flow of natural language.
• Field Classes can be configured to distinguish values within highly structured documents, but this type of extraction is better suited to simpler "extractor nodes" like quick_reference_all Value Readers or pin Data Types.
• Advances in large-language models (LLMs) have largely made Field Classes obsolete. LLM-based extraction methods in Grooper (such as AI Extract) can achieve similar results with nowhere near the amount of set up.