Validating extractions

Quality control the data extractions in a document type by writing validations using JsonLogic:

  • Test extracted fields using Boolean, logic, numeric, array, string, and other operations.
  • If Sensible extracted a field from OCR'd text, test the confidence score for the field's anchor and value as a measure of the quality of the text images. For example, test that text in a scanned document isn't blurry or illegible.

Then write your own logic based on the validations, for example:

  • pass a document extraction automatically through your pipeline if there are no errors and 10% of warning validations fail
  • flag a document extraction for human review if 5% of error validations fail

Sensible uses validation errors to calculate coverage for an extraction.

Create validations

Sensible app

To create validations in the Sensible app:

  1. Click the document type.
  2. Click Create validation.
  3. Enter the parameters for the validation.
  4. Click Create.

Parameters

A validation has the following parameters:

idvaluenotes
description (required)stringA description of the test
severity (required)error, warning, skippedThe severity of the failing test.
prerequisite fieldsarrayUse this parameter to generate skipped error messages when optional extracted fields are null. For example, if a missing broker's email address doesn't greatly affect the quality of your extraction, then write a condition to verify broker.email is properly formatted, but specify ["broker\\.email"] in this parameter to skip the verification if the email is null. For an example, see Validation 3 in the Examples section.
Double escape any dots in the field keys (for example, delivery\\.zip\\.code).
condition (required)JsonLogic objectTests extracted fields using Boolean, logic, numeric, array, string, and other operations. Supports all JsonLogic operations and extends them with Sensible operations. For the list of Sensible operations, and for more information about syntax, see the Custom Computation method.

Examples

Say that you have a document type for scanned sales quotes, called "sales_quotes", with configs for

  • company_A
  • company_B
  • company_C

You test sales quote extractions from all the companies with the following validations:

Validation 1

  • Description: If OCR'd, the source text for quoted rate value is a high-quality, unblurred image.
  • Severity: warning
  • Condition:
{"or":[
  {"not":[
    {"exists":{"var":"quote_rate.valueConfidence"}}]},
  {"and":[
    {">=": [{"var":"quote_rate.valueConfidence"},"0.90"]},
    {">=": [{"var":"quote_rate.anchorConfidence"},"0.90"]}]}
]} 

Notes: Since some sales quotes for company_A are scanned documents, check if the field came from OCR'd text. If it was OCR'd (confidence score is not null), then test that it has a high OCR confidence score for both the anchor text and the extracted value text. This validation requires that you set a high verbosity setting in the SenseML configuration.

Validation 2

  • Description: The quoted rate value isn't null

  • Severity: error

  • Condition:

{"exists":[{"var":"quote_rate.value"}]}

Notes: Tests that a field (quote_rate) isn't null using the Sensible exists operation.

Validation 3

  • Description: The quote duration is a round number
  • Severity: warning
  • Condition:{"==":[{"%":[{"var":"quote_duration.value"},2]},0]}

Notes: Retrieves the value of an extracted quote_duration field using the JsonLogic var operation, then uses the JsonLogic modulo operation (%) to divide the rate by 2 and passes the test if the remainder equals ("==") 0.

Validation 4

  • Description: Broker's email is in string@string format
  • Severity: warning
  • Prerequisite fields: ["broker\\.email"]
  • Condition: {"match":[{"var":"broker\\.email.value"},"^\\S+\\@\\S+$"]}

Notes: If broker.email isn't null, then uses a Sensible operation (match) to test that the email matches a regular expression. If broker.email is null, skips this condition.

Validation 5

  • Description: The zip code is valid for USA or CA

  • Severity: warning

  • Condition:

{"or":[
  {"and":[
    {"==":[{"var":"country.value"},"US"]},
    {"match":[{ "var":"zip_code.value" },"^[0-9]{5}$"]}]},
  {"and":[
    {"==":[{"var":"country.value"},"CA"]},
    {"match":[{"var":"zip_code.value"},"^[A-Z][0-9][A-Z] [0-9][A-Z][0-9]$"]}]}
]} 

Notes: Tests that the zip_code is a 5-digit number if the country field equals USA, or 6 alphanumeric characters if the country field equals Canada. Uses a Sensible operation (match) to test regular expressions.

Validations output

For example output of the preceding conditions, see the following extraction excerpt and validation output:

Extraction excerpt

{
	"parsed_document": {
		"quote_rate": {
			"source": "$800",
			"value": 800,
			"unit": "$",
			"type": "currency"
		},
		"quote_duration": {
			"type": "number",
			"value": "6"
		},
		"broker.email": null,
		"country": {
			"type": "string",
			"value": "USA"
		},
		"zip_code": {
			"type": "number",
			"value": "12345678901234456"
		}
}

Validations output

For the preceding extraction excerpt, Sensible outputs the following validations:

  • Validation 4: Sensible skips the broker email because the prerequisite field broker.email is null

  • Validation 5: fails because zip_code is 17 digits

{        
       "validations": [{
			"description": "The zip code is valid for USA or CA",
			"severity": "warning"
		}, {
			"description": "Broker's email is in string@string format",
			"severity": "skipped",
			"message": "Missing prerequisites: broker.email"
		}],
		"validation_summary": {
			"fields": 5,
			"fields_present": 4,
			"errors": 0,
			"warnings": 1,
			"skipped": 1
		}
	}
}