Training-Based Approaches to Document Classification (Concept) - Revision history

Dgreenwood at 14:10, 25 June 2025

2025-06-25T14:10:30Z

← Older revision		Revision as of 09:10, 25 June 2025
Line 3:		Line 3:
	There are two training based approaches in Grooper.		There are two training based approaches in Grooper.

	'''''[[~~Lexical (Classification Method)\|~~Lexical]]'''''		'''''[[Lexical]]'''''
	* This classification method trains text features (words and phrases) of examples documents. Document samples are trained as examples of a '''Document Type''' of a '''[[Content Model]]'''. Training occurs via user supervised machine learning using the [[TF-IDF]] algorithm. The '''''Lexical''''' method's '''''Text Feature Extractor''''' returns words, phrases or other results to provide possible identifiers used to classify a document. These identifiers (the words, phrases or other results from the Data Extractor used) are called "features." Document training uses [[TF-IDF]] to assign weightings to those features. During classification, Grooper looks at the weightings list for the various trained '''Document Types''' and compares them to the text features on the current document to be classified. The document is then assigned a percentage similarity score to each possible '''Document Type''' match. Whichever '''Document Type''' has the highest percentage similarity is used to classify the document.		* This classification method trains text features (words and phrases) of examples documents. Document samples are trained as examples of a '''Document Type''' of a '''[[Content Model]]'''. Training occurs via user supervised machine learning using the [[TF-IDF]] algorithm. The '''''Lexical''''' method's '''''Text Feature Extractor''''' returns words, phrases or other results to provide possible identifiers used to classify a document. These identifiers (the words, phrases or other results from the Data Extractor used) are called "features." Document training uses [[TF-IDF]] to assign weightings to those features. During classification, Grooper looks at the weightings list for the various trained '''Document Types''' and compares them to the text features on the current document to be classified. The document is then assigned a percentage similarity score to each possible '''Document Type''' match. Whichever '''Document Type''' has the highest percentage similarity is used to classify the document.
	* ''Note: This is the most common method. It is so common "training based approach" and "Lexical classification" are often used interchangeably.''		* ''Note: This is the most common method. It is so common "training based approach" and "Lexical classification" are often used interchangeably.''

	'''''[[~~Visual (Classification Method)\|~~Visual]]'''''		'''''[[Visual]]'''''
	* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.		* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.
	* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''		* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''

Randallkinard at 15:43, 22 November 2024

2024-11-22T15:43:28Z

← Older revision		Revision as of 10:43, 22 November 2024
Line 10:		Line 10:
	* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.		* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.
	* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''		* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''

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

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

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

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

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

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

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

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

	~~<u><big>'''TF-IDF'''</big></u>: {{#lst:Glossary\|TF-IDF}}~~

	~~<u><big>'''Training-Based Approaches to Document Classification'''</big></u>: {{#lst:Glossary\|Training-Based Approaches to Document Classification}}~~

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

Randallkinard at 18:28, 10 May 2024

2024-05-10T18:28:28Z

← Older revision		Revision as of 13:28, 10 May 2024
Line 10:		Line 10:
	* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.		* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.
	* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''		* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''

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

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

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

			<u><big>'''Data Extractor'''</big></u>: {{#lst:Glossary\|Data Extractor}}

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

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

			<u><big>'''IP Profile'''</big></u>: {{#lst:Glossary\|IP Profile}}

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

			<u><big>'''TF-IDF'''</big></u>: {{#lst:Glossary\|TF-IDF}}

			<u><big>'''Training-Based Approaches to Document Classification'''</big></u>: {{#lst:Glossary\|Training-Based Approaches to Document Classification}}

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

Randallkinard at 20:27, 23 April 2024

2024-04-23T20:27:01Z

← Older revision		Revision as of 15:27, 23 April 2024
Line 10:		Line 10:
	* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.		* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.
	* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''		* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''
	~~[[Category:Articles]]~~

Randallkinard at 20:26, 23 April 2024

2024-04-23T20:26:53Z

← Older revision		Revision as of 15:26, 23 April 2024
Line 1:		Line 1:
	~~<section begin="glossary" />~~		<blockquote>{{#lst:Glossary\|Training-Based Approaches to Document Classification}}</blockquote>
	<blockquote>
	~~A training based approach to document [[Classification~~\|~~classification]] classifies documents according~~ to ~~the similarity of unclassified documents to trained examples of that kind of document (or '''[[~~Document ~~Type]]''' from Grooper's perspective).~~
	</blockquote>
	~~<section end="glossary" />~~
	There are two training based approaches in Grooper.		There are two training based approaches in Grooper.

Randallkinard: Randallkinard moved page Training-Based Approaches to Document Classification to Training-Based Approaches to Document Classification (Concept): new naming convention

2024-04-18T17:40:45Z

Randallkinard moved page Training-Based Approaches to Document Classification to Training-Based Approaches to Document Classification (Concept): new naming convention

← Older revision	Revision as of 12:40, 18 April 2024
(No difference)

Dgreenwood: Dgreenwood moved page Training-Based Approach to Training-Based Approaches to Document Classification

2024-01-09T17:57:14Z

Dgreenwood moved page Training-Based Approach to Training-Based Approaches to Document Classification

← Older revision	Revision as of 12:57, 9 January 2024
(No difference)

Dgreenwood at 17:56, 9 January 2024

2024-01-09T17:56:16Z

← Older revision		Revision as of 12:56, 9 January 2024
Line 8:		Line 8:
	'''''[[Lexical (Classification Method)\|Lexical]]'''''		'''''[[Lexical (Classification Method)\|Lexical]]'''''
	* This classification method trains text features (words and phrases) of examples documents. Document samples are trained as examples of a '''Document Type''' of a '''[[Content Model]]'''. Training occurs via user supervised machine learning using the [[TF-IDF]] algorithm. The '''''Lexical''''' method's '''''Text Feature Extractor''''' returns words, phrases or other results to provide possible identifiers used to classify a document. These identifiers (the words, phrases or other results from the Data Extractor used) are called "features." Document training uses [[TF-IDF]] to assign weightings to those features. During classification, Grooper looks at the weightings list for the various trained '''Document Types''' and compares them to the text features on the current document to be classified. The document is then assigned a percentage similarity score to each possible '''Document Type''' match. Whichever '''Document Type''' has the highest percentage similarity is used to classify the document.		* This classification method trains text features (words and phrases) of examples documents. Document samples are trained as examples of a '''Document Type''' of a '''[[Content Model]]'''. Training occurs via user supervised machine learning using the [[TF-IDF]] algorithm. The '''''Lexical''''' method's '''''Text Feature Extractor''''' returns words, phrases or other results to provide possible identifiers used to classify a document. These identifiers (the words, phrases or other results from the Data Extractor used) are called "features." Document training uses [[TF-IDF]] to assign weightings to those features. During classification, Grooper looks at the weightings list for the various trained '''Document Types''' and compares them to the text features on the current document to be classified. The document is then assigned a percentage similarity score to each possible '''Document Type''' match. Whichever '''Document Type''' has the highest percentage similarity is used to classify the document.
	* ''Note: This is the most common method. It is so common "training based approach" and "~~'''~~Lexical~~'''~~ classification" are often used interchangeably.''		* ''Note: This is the most common method. It is so common "training based approach" and "Lexical classification" are often used interchangeably.''

	'''''[[Visual (Classification Method)\|Visual]]'''''		'''''[[Visual (Classification Method)\|Visual]]'''''

Dgreenwood at 17:55, 9 January 2024

2024-01-09T17:55:59Z

← Older revision		Revision as of 12:55, 9 January 2024
Line 8:		Line 8:
	'''''[[Lexical (Classification Method)\|Lexical]]'''''		'''''[[Lexical (Classification Method)\|Lexical]]'''''
	* This classification method trains text features (words and phrases) of examples documents. Document samples are trained as examples of a '''Document Type''' of a '''[[Content Model]]'''. Training occurs via user supervised machine learning using the [[TF-IDF]] algorithm. The '''''Lexical''''' method's '''''Text Feature Extractor''''' returns words, phrases or other results to provide possible identifiers used to classify a document. These identifiers (the words, phrases or other results from the Data Extractor used) are called "features." Document training uses [[TF-IDF]] to assign weightings to those features. During classification, Grooper looks at the weightings list for the various trained '''Document Types''' and compares them to the text features on the current document to be classified. The document is then assigned a percentage similarity score to each possible '''Document Type''' match. Whichever '''Document Type''' has the highest percentage similarity is used to classify the document.		* This classification method trains text features (words and phrases) of examples documents. Document samples are trained as examples of a '''Document Type''' of a '''[[Content Model]]'''. Training occurs via user supervised machine learning using the [[TF-IDF]] algorithm. The '''''Lexical''''' method's '''''Text Feature Extractor''''' returns words, phrases or other results to provide possible identifiers used to classify a document. These identifiers (the words, phrases or other results from the Data Extractor used) are called "features." Document training uses [[TF-IDF]] to assign weightings to those features. During classification, Grooper looks at the weightings list for the various trained '''Document Types''' and compares them to the text features on the current document to be classified. The document is then assigned a percentage similarity score to each possible '''Document Type''' match. Whichever '''Document Type''' has the highest percentage similarity is used to classify the document.
	* ''Note: This is the most common method. It is so common "training based approach" and "'''''Lexical''''' classification" are often used interchangeably.''		* ''Note: This is the most common method. It is so common "training based approach" and "'''Lexical''' classification" are often used interchangeably.''

	'''''[[Visual (Classification Method)\|Visual]]'''''		'''''[[Visual (Classification Method)\|Visual]]'''''

Dgreenwood at 17:55, 9 January 2024

2024-01-09T17:55:43Z

← Older revision		Revision as of 12:55, 9 January 2024
Line 6:		Line 6:
	There are two training based approaches in Grooper.		There are two training based approaches in Grooper.

	''[[Lexical (Classification Method)\|Lexical]]''		'''''[[Lexical (Classification Method)\|Lexical]]'''''
	* This classification method trains text features (words and phrases) of examples documents. Document samples are trained as examples of a '''Document Type''' of a '''[[Content Model]]'''. Training occurs via user supervised machine learning using the [[TF-IDF]] algorithm. ~~A [[Data Extractor]] set on the~~ '''''Text Feature Extractor''''' ~~property~~ returns words, phrases or other results to provide possible identifiers used to classify a document. These identifiers (the words, phrases or other results from the Data Extractor used) are called "features." Document training uses [[TF-IDF]] to assign weightings to those features. During classification, Grooper looks at the weightings list for the various trained '''Document Types''' and compares them to the text features on the current document to be classified. The document is then assigned a percentage similarity score to each possible '''Document Type''' match. Whichever '''Document Type''' has the highest percentage similarity is used to classify the document.		* This classification method trains text features (words and phrases) of examples documents. Document samples are trained as examples of a '''Document Type''' of a '''[[Content Model]]'''. Training occurs via user supervised machine learning using the [[TF-IDF]] algorithm. The '''''Lexical''''' method's '''''Text Feature Extractor''''' returns words, phrases or other results to provide possible identifiers used to classify a document. These identifiers (the words, phrases or other results from the Data Extractor used) are called "features." Document training uses [[TF-IDF]] to assign weightings to those features. During classification, Grooper looks at the weightings list for the various trained '''Document Types''' and compares them to the text features on the current document to be classified. The document is then assigned a percentage similarity score to each possible '''Document Type''' match. Whichever '''Document Type''' has the highest percentage similarity is used to classify the document.
	* ''Note: This is the most common method. It is so common "training based approach" and "Lexical classification" are often used ~~interchangibly~~.''		* ''Note: This is the most common method. It is so common "training based approach" and "'''''Lexical''''' classification" are often used interchangeably.''

	''[[Visual (Classification Method)\|Visual]]''		'''''[[Visual (Classification Method)\|Visual]]'''''
	* The ''Visual'' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.		* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.
	* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''		* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''
	[[Category:Articles]]		[[Category:Articles]]

← Older revision		Revision as of 10:43, 22 November 2024
Line 10:		Line 10:
	* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.		* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.
	* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''		* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''

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

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

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

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

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

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

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

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

	~~<u><big>'''TF-IDF'''</big></u>: {{#lst:Glossary\|TF-IDF}}~~

	~~<u><big>'''Training-Based Approaches to Document Classification'''</big></u>: {{#lst:Glossary\|Training-Based Approaches to Document Classification}}~~

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

← Older revision		Revision as of 13:28, 10 May 2024
Line 10:		Line 10:
	* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.		* The '''''Visual''''' classification method uses image data instead of text data to determine the '''Document Type'''. Instead of using text extractors, an '''[[IP Profile]]''' will be set with an '''Extract Features''' command to get data pertaining to a document's image. Document samples are trained as examples of a '''Document Type'''.
	* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''		* ''Note: While this is a much less commonly used method, it is still technically a training based approach to classification.''

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

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

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

			<u><big>'''Data Extractor'''</big></u>: {{#lst:Glossary\|Data Extractor}}

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

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

			<u><big>'''IP Profile'''</big></u>: {{#lst:Glossary\|IP Profile}}

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

			<u><big>'''TF-IDF'''</big></u>: {{#lst:Glossary\|TF-IDF}}

			<u><big>'''Training-Based Approaches to Document Classification'''</big></u>: {{#lst:Glossary\|Training-Based Approaches to Document Classification}}

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