2.72:What is Classification - DSmith

From Grooper Wiki

Overview

Classification is an Activity in Grooper that allows the assigning of a Content Type to a Document. While we as humans may be able to classify a document by reading it (or its title should it have one), to Grooper all documents that come in are unclassified, or "blank". If we want Grooper to know what a Purchase Order is, or be able to tell the difference between a Purchase Order and an invoice, we have to tell it; and we do that through Classification.


Why Classify?

Why is classification necessary? Why does it matter in Grooper? Isn't enough for humans to look at a document in a document folder and know what it is? No. Why? Well, as a user, you would want properly extracted information, wouldn't you? How else would you expect Grooper to extract an invoice number from an invoice and a patient name from a medical history form when Grooper can't even tell the difference between the two?

Classification Methods

In order to classify a document, you must choose between four different Classification Methods. They are:

  • Rules-Based
  • Lableset-Based
  • Lexical
  • Visual



These methods can be set on the Content Model via the Classification Method property. Whatever method you choose is largely based on what sort of document you have; its structure and complexity. We will provide a brief overview of each Classification Method here.

For more detailed information about each Classification Method, click the following links:

Rules-Based

Documents have ways of identifying themselves to a human reader. This can be through a title, labels, content, or all of the above. We can make use of this information and set it up as rules that Grooper can then use to classify documents. What are these rules? Whatever we tell Grooper they are. Take a look at these two documents:

Labelset-Based

Labels are a great way for humans to organize, categorize, and digest information. The same can be said for labels when it comes to Grooper. Lableset-Based Classification is good for document types where the information is exactly the same, but the labels might be different.

For example, look at these two invoices. The information is exactly the same, but the labels that categorize the invoice number are different.



Of course, Lableset-Based Classification can be used with just about any document that organizes its information using labels.

Lexical

Unfortunately, not every document is structured, or has labels to help out both humans and Grooper with classification. This is where Lexical Classification comes in. Here, we have some academic papers on statistics.

To a human eye, they have structure, but not in a way that Grooper can make use of with Visual or Rules-Based Classification; and there's certainly no labels for Labelset-Based to make use of. Thus, we can use Lexical Classification. Perhaps have Grooper look for repeating words, like "statistics" or "statistical method"

Visual

Visual Classification is different. Unlike the previous three methods mentioned here, Visual Classification relies upon the structure of the document itself rather than the language present on the document. Take a look at these two documents here. Instead of focusing on labels, titles, or a piece of recurring text, we can have Grooper concentrate on how the pixels are grouped together and classify documents that way.

We can tell Grooper that documents structured like this are invoices

And documents structured like this are legal documents.

Unfortunately, if our documents are similar in structure, then Grooper will have difficulty classifying them, and may even classify them as the same document. Such is the downside to Visual Classification.