2023.1:Mixed Classification (Concept) - Revision history

Dgreenwood at 21:33, 12 May 2025

2025-05-12T21:33:15Z

← Older revision		Revision as of 16:33, 12 May 2025
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	== ABOUT ==		== ABOUT ==
	When classifying a '''Batch''' with varying types of '''Documents''', you may need to rely on more than just the ~~'''''Classification~~ Method~~'''''~~ selected on the '''Content Model'''. This is often common when it comes to training-based ~~'''''Classification~~ Methods~~'''''~~ (like the '''''[[Lexical]]''''' method). Grooper classifies '''Documents''' based on a confidence variable called a "Similarity Score". If "Document Type A" scores higher than "Document Type B", then the classification for "Document Type A" wins, regardless of whether or not that classification is actually correct.		When classifying a '''Batch''' with varying types of '''Documents''', you may need to rely on more than just the Classify Method selected for the '''Content Model'''. This is often common when it comes to training-based Classify Methods (like the '''''[[Lexical]]''''' method). Grooper classifies '''Documents''' based on a confidence variable called a "Similarity Score". If "Document Type A" scores higher than "Document Type B", then the classification for "Document Type A" wins, regardless of whether or not that classification is actually correct.

	Thankfully, you can configure a "rules-based" extractor on a particular '''Document Type''' to work in tandem with the training-based ~~'''''Classification Method'''''~~. This "Mixed Classification" approach ensures not only does the '''Batch''' get classified as a whole but false positives are also avoided. Documents that would have been misclassified are assigned the proper '''Document Type''' via that '''Document Type's''' '''''Positive Extractor'''''.		Thankfully, you can configure a "rules-based" extractor on a particular '''Document Type''' to work in tandem with the training-based Classify Methods. This "Mixed Classification" approach ensures not only does the '''Batch''' get classified as a whole but false positives are also avoided. Documents that would have been misclassified are assigned the proper '''Document Type''' via that '''Document Type's''' '''''Positive Extractor'''''.

	== The Problem ==		== The Problem ==
	In this example, we have a '''Batch''' of documents that were classified using the ''Lexical ~~Classification Method''~~. Everything was classified properly, except for one document in the "Assignment" folder.		In this example, we have a '''Batch''' of documents that were classified using the Lexical method. Everything was classified properly, except for one document in the "Assignment" folder.
	<br>		<br>
	<br>		<br>
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	== The Solution: Mixing Classification ==		== The Solution: Mixing Classification ==
	So, we have an Assignment that was misclassified as a Memo. Training the document and reclassification could cause more issues in the long run, so what's to be done? This is where the concept of Mixing Classification comes in. We'll configure the '''''Positive Extractor''''' on the '''Document Type''' and have it work in tandem with the ~~'''''Classification~~ Method~~'''''~~ so that every Document is properly classified.		So, we have an Assignment that was misclassified as a Memo. Training the document and reclassification could cause more issues in the long run, so what's to be done? This is where the concept of Mixing Classification comes in. We'll configure the '''''Positive Extractor''''' on the '''Document Type''' and have it work in tandem with the Classify Method so that every Document is properly classified.
	<br>		<br>
	<br>		<br>
	# Select the '''Document Type''' which the problem Document was supposed to be classified as. In our case, that would be the "Assignment" '''Document Type'''.		# Select the '''Document Type''' which the problem Document was supposed to be classified as. In our case, that would be the "Assignment" '''Document Type'''.
	# Remain on the Document Type tab.		# Remain on the Document Type tab.
	# In addition to the ~~'''''Classification~~ Method~~''''' one can choose on~~ the '''''Content Model''''', the '''Document Type''' has its own Classification section. Here, one can configure both '''''Positive''''' and '''''Negative Extractors'''''. These '''''Extractors''''' work by either positively identifying data that you want to be extracted and thus used to classify a document as a particular '''Document Type''', or to exclude text data from classification. Here, we'll be configuring the '''''Positive Extractor'''''. Select the hamburger icon at the far right of the property to expand the drop-down menu.		# In addition to the Classify Method chosen for the '''''Content Model''''', the '''Document Type''' has its own Classification section. Here, one can configure both '''''Positive''''' and '''''Negative Extractors'''''. These '''''Extractors''''' work by either positively identifying data that you want to be extracted and thus used to classify a document as a particular '''Document Type''', or to exclude text data from classification. Here, we'll be configuring the '''''Positive Extractor'''''. Select the hamburger icon at the far right of the property to expand the drop-down menu.
	# Select List Match.		# Select List Match.
	[[File:2023.1_Mixed_Classification_03_The_Solution_Mixed_Classification_01(1).png]]		[[File:2023.1_Mixed_Classification_03_The_Solution_Mixed_Classification_01(1).png]]
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	<br>		<br>
	<br>		<br>
	As you can see, by combining both the chosen ~~'''''Classification~~ Method~~'''''~~ with the configured ''''Positive Extractor''''' on the '''Document Type''', we can properly classify problematic documents that just the ~~'''''Classification~~ Method~~'''''~~ or '''''Positive Extractor''''' alone would not be able to do. By mixing the rules-based '''''Positive Extractor''''' with the training-based Lexical ~~'''''Classification Method'''''~~, we have ensured that each document is assigned the correct '''Document Type'''.		As you can see, by combining both the chosen Classify Method with the configured ''''Positive Extractor''''' on the '''Document Type''', we can properly classify problematic documents that just the Classify Method or '''''Positive Extractor''''' alone would not be able to do. By mixing the rules-based '''''Positive Extractor''''' with the training-based Lexical method, we have ensured that each document is assigned the correct '''Document Type'''.

Randallkinard at 21:50, 21 November 2024

2024-11-21T21:50:52Z

← Older revision		Revision as of 16:50, 21 November 2024
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	<br>		<br>
	As you can see, by combining both the chosen '''''Classification Method''''' with the configured ''''Positive Extractor''''' on the '''Document Type''', we can properly classify problematic documents that just the '''''Classification Method''''' or '''''Positive Extractor''''' alone would not be able to do. By mixing the rules-based '''''Positive Extractor''''' with the training-based Lexical '''''Classification Method''''', we have ensured that each document is assigned the correct '''Document Type'''.		As you can see, by combining both the chosen '''''Classification Method''''' with the configured ''''Positive Extractor''''' on the '''Document Type''', we can properly classify problematic documents that just the '''''Classification Method''''' or '''''Positive Extractor''''' alone would not be able to do. By mixing the rules-based '''''Positive Extractor''''' with the training-based Lexical '''''Classification Method''''', we have ensured that each document is assigned the correct '''Document Type'''.

	~~== Glossary ==~~
	~~<u><big>'''AND'''</big></u>: {{#lst:Glossary\|AND}}~~

	~~<u><big>'''Batch Process Step'''</big></u>: {{#lst:Glossary\|Batch Process Step}}~~

	~~<u><big>'''Batch'''</big></u>: {{#lst:Glossary\|Batch}}~~

	~~<u><big>'''Classification Method'''</big></u>: {{#lst:Glossary\|Classification Method}}~~

	~~<u><big>'''Classification'''</big></u>: {{#lst:Glossary\|Classification}}~~

	~~<u><big>'''Classify'''</big></u>: {{#lst:Glossary\|Classify}}~~

	~~<u><big>'''Combine'''</big></u>: {{#lst:Glossary\|Combine}}~~

	~~<u><big>'''Content Model'''</big></u>: {{#lst:Glossary\|Content Model}}~~

	~~<u><big>'''Document Type'''</big></u>: {{#lst:Glossary\|Document Type}}~~

	~~<u><big>'''Extract'''</big></u>: {{#lst:Glossary\|Extract}}~~

	~~<u><big>'''Lexical'''</big></u>: {{#lst:Glossary\|Lexical}}~~

	~~<u><big>'''List Match'''</big></u>: {{#lst:Glossary\|List Match}}~~

	~~<u><big>'''Project'''</big></u>: {{#lst:Glossary\|Project}}~~

	~~<u><big>'''Vertical Wrap'''</big></u>: {{#lst:Glossary\|Vertical Wrap}}~~

Dgreenwood at 17:40, 25 September 2024

2024-09-25T17:40:21Z

← Older revision		Revision as of 12:40, 25 September 2024
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	{{AutoVersion}}		{{AutoVersion}}

	<blockquote>{{#lst:Glossary\|~~Combined Methods~~}}</blockquote>		<blockquote>{{#lst:Glossary\|Mixed Classification}}</blockquote>
	<br>		<br>
	{\|class="download-box"		{\|class="download-box"
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	== ABOUT ==		== ABOUT ==
	When classifying a '''Batch''' with varying types of '''Documents''', ~~it's inevitable that~~ you~~'ll~~ need to rely on more than just the '''''Classification Method''''' selected on the '''Content Model'''~~; especially~~ when it comes to training-based '''''Classification Methods'''''. Grooper classifies '''Documents''' based on a confidence variable called a Similarity Score. If "Document Type A" scores higher than "Document Type B", then the classification for "Document Type A" wins, regardless of whether or not that classification is actually correct.		When classifying a '''Batch''' with varying types of '''Documents''', you may need to rely on more than just the '''''Classification Method''''' selected on the '''Content Model'''. This is often common when it comes to training-based '''''Classification Methods''''' (like the '''''[[Lexical]]''''' method). Grooper classifies '''Documents''' based on a confidence variable called a "Similarity Score". If "Document Type A" scores higher than "Document Type B", then the classification for "Document Type A" wins, regardless of whether or not that classification is actually correct.

	Thankfully, you can configure a rules-based ~~Extractor~~ on a particular '''Document Type''' to work in tandem with the training-based '''''Classification Method'''''. This "Mixed Classification" approach ensures ~~that~~ not only does the '''Batch''' get classified as a whole, but ~~that~~ false positives are avoided ~~and documents~~ that would have been misclassified are assigned the proper '''Document Type''' via ~~the~~ '''''Positive Extractor'''''.		Thankfully, you can configure a "rules-based" extractor on a particular '''Document Type''' to work in tandem with the training-based '''''Classification Method'''''. This "Mixed Classification" approach ensures not only does the '''Batch''' get classified as a whole but false positives are also avoided. Documents that would have been misclassified are assigned the proper '''Document Type''' via that '''Document Type's''' '''''Positive Extractor'''''.

	== The Problem ==		== The Problem ==

Randallkinard: Randallkinard moved page Mixed Classification (Concept) to 2023.1:Mixed Classification (Concept)

2024-08-28T16:45:26Z

Randallkinard moved page Mixed Classification (Concept) to 2023.1:Mixed Classification (Concept)

← Older revision	Revision as of 11:45, 28 August 2024
(No difference)

Randallkinard at 14:52, 27 August 2024

2024-08-27T14:52:48Z

@@ Line 11: / Line 11: @@
 * [[Media:2023.1_Wiki_Mixed-Classification_Project.zip]]
 |}
 == ABOUT ==
@@ Line 85: / Line 56: @@
 <br>
 As you can see, by combining both the chosen '''''Classification Method''''' with the configured ''''Positive Extractor''''' on the '''Document Type''', we can properly classify problematic documents that just the '''''Classification Method''''' or '''''Positive Extractor''''' alone would not be able to do. By mixing the rules-based '''''Positive Extractor''''' with the training-based Lexical '''''Classification Method''''', we have ensured that each document is assigned the correct '''Document Type'''.

Dgreenwood at 13:53, 13 June 2024

2024-06-13T13:53:15Z

← Older revision		Revision as of 08:53, 13 June 2024
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	<u><big>'''Combine'''</big></u>: {{#lst:Glossary\|Combine}}		<u><big>'''Combine'''</big></u>: {{#lst:Glossary\|Combine}}

	~~<u><big>'''Combined Methods'''</big></u>: {{#lst:Glossary\|Combined Methods}}~~

	<u><big>'''Content Model'''</big></u>: {{#lst:Glossary\|Content Model}}		<u><big>'''Content Model'''</big></u>: {{#lst:Glossary\|Content Model}}

Randallkinard at 16:03, 10 May 2024

2024-05-10T16:03:51Z

@@ Line 11: / Line 11: @@
 * [[Media:2023.1_Wiki_Mixed-Classification_Project.zip]]
 |}
 == ABOUT ==

Dsmith: /* The Problem */

2024-04-29T16:08:00Z

The Problem

← Older revision		Revision as of 11:08, 29 April 2024
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	<br>		<br>
	<br>		<br>
	# Here, we have an ~~example~~ '''~~Batch~~''' ~~containing classified folders and classified documents~~.		# Here, we have a document that was supposed to be classified as an 'Assignment' '''Document Type''', but has been misclassified as a 'Memo' '''Document Type.'''
	# Unfortunately, ~~one of~~ the ~~documents within the "Assignments" folder has been misclassified~~ as a ~~memo~~.		# This is due to the Similarity Score for Memo coming in at 61%, which is higher than the Assignment score that only came in at 55%. Unfortunately, since the incorrect '''Document Type''' scored higher, this document was classified as a Memo.
	# We could train the 'Document as the "Assignment" '''Document Type''' and reclassify everything once more, but since the only real "Assignment" part of the document is the small, highlighted portion, it could cause issues for classification overall.		# We could train the Document as the "Assignment" '''Document Type''' and reclassify everything once more, but since the only real "Assignment" part of the document is the small, highlighted portion, it could cause issues for classification overall.
	#* In fact, since this particular document bears a striking resemblance to a memo, it might mess up classification with the memos as well. So, training and reclassification is out of the question.		#* In fact, since this particular document bears a striking resemblance to a memo, it might mess up classification with the memos as well. So, training and reclassification is out of the question.
	[[File:2023.1_Mixed_Classification_(Concept)_02_The_Problem_01(1).png]]		[[File:2023.1_Mixed_Classification_(Concept)_02_The_Problem_01(1).png]]

Dsmith: /* The Result */

2024-04-29T16:01:32Z

The Result

← Older revision		Revision as of 11:01, 29 April 2024
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	<br>		<br>
	# Voila! Thanks to the configurations on the '''''Positive Extractor''''', our Document has now been properly classified as an "Assignment" '''Document Type'''.		# Voila! Thanks to the configurations on the '''''Positive Extractor''''', our Document has now been properly classified as an "Assignment" '''Document Type'''.
	[[File:2023.1_Mixed_Classification_(Concept)_04_The_Result_01.png]]		[[File:2023.1_Mixed_Classification_(Concept)_04_The_Result_01(1).png]]
	<br>		<br>
	<br>		<br>
	As you can see, by combining both the chosen '''''Classification Method''''' with the configured ''''Positive Extractor''''' on the '''Document Type''', we can properly classify problematic documents that just the '''''Classification Method''''' or '''''Positive Extractor''''' alone would not be able to do. By mixing the rules-based '''''Positive Extractor''''' with the training-based Lexical '''''Classification Method''''', we have ensured that each document is assigned the correct '''Document Type'''.		As you can see, by combining both the chosen '''''Classification Method''''' with the configured ''''Positive Extractor''''' on the '''Document Type''', we can properly classify problematic documents that just the '''''Classification Method''''' or '''''Positive Extractor''''' alone would not be able to do. By mixing the rules-based '''''Positive Extractor''''' with the training-based Lexical '''''Classification Method''''', we have ensured that each document is assigned the correct '''Document Type'''.

Dsmith: /* The Solution: Mixing Classification */

2024-04-29T16:00:52Z

The Solution: Mixing Classification

← Older revision		Revision as of 11:00, 29 April 2024
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	# In addition to the '''''Classification Method''''' one can choose on the '''''Content Model''''', the '''Document Type''' has its own Classification section. Here, one can configure both '''''Positive''''' and '''''Negative Extractors'''''. These '''''Extractors''''' work by either positively identifying data that you want to be extracted and thus used to classify a document as a particular '''Document Type''', or to exclude text data from classification. Here, we'll be configuring the '''''Positive Extractor'''''. Select the hamburger icon at the far right of the property to expand the drop-down menu.		# In addition to the '''''Classification Method''''' one can choose on the '''''Content Model''''', the '''Document Type''' has its own Classification section. Here, one can configure both '''''Positive''''' and '''''Negative Extractors'''''. These '''''Extractors''''' work by either positively identifying data that you want to be extracted and thus used to classify a document as a particular '''Document Type''', or to exclude text data from classification. Here, we'll be configuring the '''''Positive Extractor'''''. Select the hamburger icon at the far right of the property to expand the drop-down menu.
	# Select List Match.		# Select List Match.
	[[File:2023.1_Mixed_Classification_03_The_Solution_Mixed_Classification_01.png]]		[[File:2023.1_Mixed_Classification_03_The_Solution_Mixed_Classification_01(1).png]]
	<br>		<br>
	<br>		<br>

← Older revision		Revision as of 16:50, 21 November 2024
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	<br>		<br>
	As you can see, by combining both the chosen '''''Classification Method''''' with the configured ''''Positive Extractor''''' on the '''Document Type''', we can properly classify problematic documents that just the '''''Classification Method''''' or '''''Positive Extractor''''' alone would not be able to do. By mixing the rules-based '''''Positive Extractor''''' with the training-based Lexical '''''Classification Method''''', we have ensured that each document is assigned the correct '''Document Type'''.		As you can see, by combining both the chosen '''''Classification Method''''' with the configured ''''Positive Extractor''''' on the '''Document Type''', we can properly classify problematic documents that just the '''''Classification Method''''' or '''''Positive Extractor''''' alone would not be able to do. By mixing the rules-based '''''Positive Extractor''''' with the training-based Lexical '''''Classification Method''''', we have ensured that each document is assigned the correct '''Document Type'''.

	~~== Glossary ==~~
	~~<u><big>'''AND'''</big></u>: {{#lst:Glossary\|AND}}~~

	~~<u><big>'''Batch Process Step'''</big></u>: {{#lst:Glossary\|Batch Process Step}}~~

	~~<u><big>'''Batch'''</big></u>: {{#lst:Glossary\|Batch}}~~

	~~<u><big>'''Classification Method'''</big></u>: {{#lst:Glossary\|Classification Method}}~~

	~~<u><big>'''Classification'''</big></u>: {{#lst:Glossary\|Classification}}~~

	~~<u><big>'''Classify'''</big></u>: {{#lst:Glossary\|Classify}}~~

	~~<u><big>'''Combine'''</big></u>: {{#lst:Glossary\|Combine}}~~

	~~<u><big>'''Content Model'''</big></u>: {{#lst:Glossary\|Content Model}}~~

	~~<u><big>'''Document Type'''</big></u>: {{#lst:Glossary\|Document Type}}~~

	~~<u><big>'''Extract'''</big></u>: {{#lst:Glossary\|Extract}}~~

	~~<u><big>'''Lexical'''</big></u>: {{#lst:Glossary\|Lexical}}~~

	~~<u><big>'''List Match'''</big></u>: {{#lst:Glossary\|List Match}}~~

	~~<u><big>'''Project'''</big></u>: {{#lst:Glossary\|Project}}~~

	~~<u><big>'''Vertical Wrap'''</big></u>: {{#lst:Glossary\|Vertical Wrap}}~~

← Older revision		Revision as of 08:53, 13 June 2024
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	<u><big>'''Combine'''</big></u>: {{#lst:Glossary\|Combine}}		<u><big>'''Combine'''</big></u>: {{#lst:Glossary\|Combine}}

	~~<u><big>'''Combined Methods'''</big></u>: {{#lst:Glossary\|Combined Methods}}~~

	<u><big>'''Content Model'''</big></u>: {{#lst:Glossary\|Content Model}}		<u><big>'''Content Model'''</big></u>: {{#lst:Glossary\|Content Model}}