2.90:Confidence Multiplier and Output Confidence (Property)
Some results carry more weight than others.
About
Weighted rules is an informal title given to the practical application of the Confidence Multiplier property of a Data Type. Its practical application allows a user to arbitrarily set the confidence of a result of a particular Data Type in order to allow that Data Type to appear more (or less) favorable to a parent Data Type that is leveraging the Order By property configured to the Confidence setting.
Use Cases
Weighted Rules can be used in cases where one is trying to find an element of data which can appear on many similar types of forms that do not have a consistent method to identify where the data is.
For Example, on different forms, the best method to pick up a piece of data may be a Key-Value Pair, a Field Class, a simple pattern match, a pattern match leveraging FuzzyRegEx, or some other method.
One of the more recent methodologies for incorporating multiple extractors to return a single field value is referred to as Waterfall Extraction. The Waterfall Extraction technique returns results by organizing multiple extractors (and their numerous configurations) under a parent Data Type. The extractors results are prioritized according to the Order By property on the parent Data Type. The Order By property of the parent Data Type can be set to the following: Position, Frequency, Confidence, Extractor, Length, Value.
Setting Order By to Confidence can prioritize "non-Fuzzy results" over "Fuzzy" results or prioritize the most confident result of the child extractors leveraging FuzzyRegEx. However, extractors using traditional, non-Fuzzy regex always return their results at 100%. The confidence of a returned result has, historically, only been affected in one of two ways:
- Data Types (or a child Data Format's) regular expression pattern leverages FuzzyRegEx. Characters are mutated to match the pattern, either inserted, deleted, or swapped. Each mutation comes at the cost of the result's overall confidence, generating a result less than 100% confident.
- Field Classes, by design leverage trained/weighted features and should not return results at 100% confidence
Considering this, a properly configured extractor can, and does, return results below 100%, and can break the logical approach of organizing results by confidence. A result returned at 90% confidence could be more desirable than one returned at 100%.
We will explore how and why in the How To section of this article.
How To
Here we'll explore a use case using a mortgage document.
In this example, an OCR error produced a misread the words “final loan” by not recognizing the space between them.
Three Data Types were established to find variations of a result.
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FinalLoan A Data Type which uses a regular expression looking for the expression “finalloan” with no spaces. |
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Final Loan A Data Type which uses a regular expression looking for the expression “final loan” with the space. |
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Fuzzy: Final Loan A Data Type which uses a fuzzy regular expression looking for the expression “final loan” with the space. |
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The Waterfall Extractor is a Data Type that is a parent or references all of the unique extractors for a piece of data and then determines which one should be given as a final output to a Data Field.
Using Order By set to Confidence and Direction set to Descending as the sort criteria, two extractors match with the highest confidence result given first. The FinalLoan extractor matched because it found “finalloan” with no spaces and it is not leveraging FuzzyRegEx, so it matched at 100%. The Final Loan extractor did not match, because it is not using FuzzyRegEx and it did not find a space between the two words so it did not consider it a match. The Fuzzy: Final Loan, leveraging FuzzyRegEx, matched because it was able to make the word “finalloan” into “final loan” by inserting a space and so it was a 90% match.
We would like the actual correct result of final loan to win. There are two ways to do this. One way would be to bump up the confidence of the fuzzy regular expression Data Type Fuzzy: Final Loan. This is done by modifying the Confidence Multiplier property in the Result Options' of the Fuzzy: Final Loan Data Type .
That works for this case, but what if there was another document where the OCR read the space between the two words correctly. In that case, the result from the Final Loan Data Type would match at 100%, and the Fuzzy: Final Loan Data Type, with the Confidence Multiplier property set to 1.2 would match at 120%. While this would technically yield the correct result, it is generally best practice to have the exact match return the highest percentage. There are a couple of ways to tackle this situation. One way would be to bump up the Confidence Multiplier property on the Final Loan Data Type to something like 1.3 But another way, would be to reduce the Confidence Multiplier property on the FinalLoan Data Type so that it returns less than 90%.
Let's change some settings to set this extractor up to return the results in the desired way; that being with the most right result weighted the highest.
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Reset the Confidence Multiplier property in the Result Options property window for the Fuzzy: Final Loan Data Type. |
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Set the Confidence Multiplier property in the Result Options property window for the FinalLoan Data Type to 0.75. The results on the parent Data Type will now show the un-weighted Data Type Fuzzy: Final Loan at a confidence of 90% (again, because a space was inserted), and the FinalLoan Data Type will show 75%.
In the event another document is OCRed correctly with a space between the words, the Final Loan Data Type would return the exact match at 100%. The Fuzzy: Final Loan Data Type would also return 100% because the expression matched 100% with no substitutions. In order to make the exact match always preferred, it would also be possible to set the Fuzzy: Final Loan Data Type Confidence Multiplier property to 0.99. But since both the fuzzy and the exact non-fuzzy Data Type matched 100%, it doesn’t really matter which one returns the result. |
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General Usage
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, so you can use a value of, for example 0.5 to 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.
In Context - Waterfall Classification
Setting the Classification Method property on a Content Model to Rules-Based, one can set up Data Types as Positive Extractors and Negative Extractors, either of which can be Waterfall Extractors just by having child Data Types or referencing other Data Types or Field Classes. In this case, the system is looking for some result to be returned. In the Waterfall Classification method, the Minimum Confidence property can be set in the Result Filter property window of a Data Type which will eliminate any results less than that confidence. This may eliminate the results of some referenced extractors which technically matched, but at a low percent. If we happen to know that those low percentage match hits are valid, then the Confidence Multipliers on those referenced Data Types can be set to a higher value in order to make them hit the Minimum Confidence required. Similarly, if it is desired to discount a high confidence of some extractors which are hitting on the wrong Document Type, the Confidence Multiplier property can be reduced so that those Data Types only exceed the Minimum Confidence when they are very high confidence.
Version Differences
Prior to Grooper 2.9 the Confidence Multiplier property did not exist.