Orange: Similarity Hashing - Revision history

Onnowpurbo: /* Contoh */

2020-04-12T02:54:26Z

Contoh

← Older revision		Revision as of 02:54, 12 April 2020
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	Kita akan menggunakan file deerwester.tab untuk memperoleh dokumen yang sama dari corpus yang kecil ini. Load data menggunakan widget Corpus dan kirim ke widget Similarity Hashing. Kita menggunakan nilai default hash size dan default shingle length. Kita bisa mengamati keluaran widget Similarity Hashing di widget Data Table. Ada 64 atribut baru yang tersedia, terkait dengan Simhash size parameter.		Kita akan menggunakan file deerwester.tab untuk memperoleh dokumen yang sama dari corpus yang kecil ini. Load data menggunakan widget Corpus dan kirim ke widget Similarity Hashing. Kita menggunakan nilai default hash size dan default shingle length. Kita bisa mengamati keluaran widget Similarity Hashing di widget Data Table. Ada 64 atribut baru yang tersedia, terkait dengan Simhash size parameter.

	[[File:Similarity-Hashing-Example.png\|center\|~~200px~~\|thumb]]		[[File:Similarity-Hashing-Example.png\|center\|600px\|thumb]]

	==Referensi==		==Referensi==

Onnowpurbo: /* Contoh */

2020-04-12T02:54:19Z

Contoh

← Older revision		Revision as of 02:54, 12 April 2020
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	==Contoh==		==Contoh==

	Kita akan menggunakan file deerwester.tab untuk memperoleh dokumen yang sama dari corpus yang kecil ini. Load data menggunakan widget Corpus dan kirim ~~kan~~ ke widget Similarity Hashing. Kita menggunakan nilai default hash size dan default shingle length. Kita bisa mengamati keluaran widget Similarity Hashing di widget Data Table. Ada 64 atribut baru yang tersedia, terkait dengan Simhash size parameter.		Kita akan menggunakan file deerwester.tab untuk memperoleh dokumen yang sama dari corpus yang kecil ini. Load data menggunakan widget Corpus dan kirim ke widget Similarity Hashing. Kita menggunakan nilai default hash size dan default shingle length. Kita bisa mengamati keluaran widget Similarity Hashing di widget Data Table. Ada 64 atribut baru yang tersedia, terkait dengan Simhash size parameter.

	[[File:Similarity-Hashing-Example.png\|center\|200px\|thumb]]		[[File:Similarity-Hashing-Example.png\|center\|200px\|thumb]]


	==Referensi==		==Referensi==

Onnowpurbo at 10:51, 11 April 2020

2020-04-11T10:51:59Z

← Older revision		Revision as of 10:51, 11 April 2020
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	~~Computes documents hashes~~.		Widget Similarity Hashing menghitung nilai hash dokumen.

	==Input==		==Input==
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	Corpus: Corpus with simhash value as attributes.		Corpus: Corpus with simhash value as attributes.

	Similarity Hashing ~~is a widget that transforms documents into~~ similarity ~~vectors~~. ~~The widget uses~~ SimHash ~~method from from~~ Moses Charikar.		Widget Similarity Hashing akan mentranformasikan dokumen menjadi vector similarity. Widget Similarity Hashing menggunakan metoda SimHash dari Moses Charikar.

	[[File:Similarity-Hashing-stamped.png\|center\|~~200px~~\|thumb]]		[[File:Similarity-Hashing-stamped.png\|center\|400px\|thumb]]

	* Set Simhash size (how many attributes will be on the output, corresponds to bits of information) and shingle length (how many tokens are used in a shingle).		* Set Simhash size (how many attributes will be on the output, corresponds to bits of information) and shingle length (how many tokens are used in a shingle).
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	==Contoh==		==Contoh==

	~~We will use~~ deerwester.tab ~~to find similar documents in this small~~ corpus. Load ~~the~~ data ~~with~~ Corpus ~~and pass it to~~ Similarity Hashing. ~~We will keep the~~ default hash size ~~and~~ shingle length. ~~We can observe what the~~ widget ~~outputs in a~~ Data Table. ~~There are~~ 64 ~~new attributes available~~, ~~corresponding to the~~ Simhash size parameter.		Kita akan menggunakan file deerwester.tab untuk memperoleh dokumen yang sama dari corpus yang kecil ini. Load data menggunakan widget Corpus dan kirim kan ke widget Similarity Hashing. Kita menggunakan nilai default hash size dan default shingle length. Kita bisa mengamati keluaran widget Similarity Hashing di widget Data Table. Ada 64 atribut baru yang tersedia, terkait dengan Simhash size parameter.

	[[File:Similarity-Hashing-Example.png\|center\|200px\|thumb]]		[[File:Similarity-Hashing-Example.png\|center\|200px\|thumb]]

Onnowpurbo at 03:27, 29 January 2020

2020-01-29T03:27:31Z

← Older revision		Revision as of 03:27, 29 January 2020
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	Computes documents hashes.		Computes documents hashes.

	~~Inputs~~		==Input==

	Corpus: A collection of documents.		Corpus: A collection of documents.

	~~Outputs~~		==Output==

	Corpus: Corpus with simhash value as attributes.		Corpus: Corpus with simhash value as attributes.

	Similarity Hashing is a widget that transforms documents into similarity vectors. The widget uses SimHash method from from Moses Charikar.		Similarity Hashing is a widget that transforms documents into similarity vectors. The widget uses SimHash method from from Moses Charikar.
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	[[File:Similarity-Hashing-stamped.png\|center\|200px\|thumb]]		[[File:Similarity-Hashing-stamped.png\|center\|200px\|thumb]]

	Set Simhash size (how many attributes will be on the output, corresponds to bits of information) and shingle length (how many tokens are used in a shingle).		* Set Simhash size (how many attributes will be on the output, corresponds to bits of information) and shingle length (how many tokens are used in a shingle).
	Commit Automatically output the data automatically. Alternatively, press Commit.		* Commit Automatically output the data automatically. Alternatively, press Commit.

	==Contoh==		==Contoh==

Onnowpurbo at 08:56, 24 January 2020

2020-01-24T08:56:30Z

← Older revision		Revision as of 08:56, 24 January 2020
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	Similarity Hashing is a widget that transforms documents into similarity vectors. The widget uses SimHash method from from Moses Charikar.		Similarity Hashing is a widget that transforms documents into similarity vectors. The widget uses SimHash method from from Moses Charikar.

	~~../_images/~~Similarity-Hashing-stamped.png		[[File:Similarity-Hashing-stamped.png\|center\|200px\|thumb]]

	Set Simhash size (how many attributes will be on the output, corresponds to bits of information) and shingle length (how many tokens are used in a shingle).		Set Simhash size (how many attributes will be on the output, corresponds to bits of information) and shingle length (how many tokens are used in a shingle).
	Commit Automatically output the data automatically. Alternatively, press Commit.		Commit Automatically output the data automatically. Alternatively, press Commit.

	~~Example~~		==Contoh==

	We will use deerwester.tab to find similar documents in this small corpus. Load the data with Corpus and pass it to Similarity Hashing. We will keep the default hash size and shingle length. We can observe what the widget outputs in a Data Table. There are 64 new attributes available, corresponding to the Simhash size parameter.		We will use deerwester.tab to find similar documents in this small corpus. Load the data with Corpus and pass it to Similarity Hashing. We will keep the default hash size and shingle length. We can observe what the widget outputs in a Data Table. There are 64 new attributes available, corresponding to the Simhash size parameter.

	~~../_images/~~Similarity-Hashing-Example.png		[[File:Similarity-Hashing-Example.png\|center\|200px\|thumb]]
	~~References~~

			==Referensi==

	Charikar, M. (2002) Similarity estimation techniques from rounding algorithms. STOC ‘02 Proceedings of the thirty-fourth annual ACM symposium on Theory of computing, p. 380-388.		Charikar, M. (2002) Similarity estimation techniques from rounding algorithms. STOC ‘02 Proceedings of the thirty-fourth annual ACM symposium on Theory of computing, p. 380-388.

Onnowpurbo: Created page with "Sumber: https://orange3-text.readthedocs.io/en/latest/widgets/similarityhashing.html Computes documents hashes. Inputs Corpus: A collection of documents. Outputs..."

2020-01-17T08:25:00Z

Created page with "Sumber: https://orange3-text.readthedocs.io/en/latest/widgets/similarityhashing.html Computes documents hashes. Inputs Corpus: A collection of documents. Outputs..."

New page

Sumber: https://orange3-text.readthedocs.io/en/latest/widgets/similarityhashing.html

Computes documents hashes.

Inputs

Corpus: A collection of documents.

Outputs

Corpus: Corpus with simhash value as attributes.

Similarity Hashing is a widget that transforms documents into similarity vectors. The widget uses SimHash method from from Moses Charikar.

../_images/Similarity-Hashing-stamped.png

Set Simhash size (how many attributes will be on the output, corresponds to bits of information) and shingle length (how many tokens are used in a shingle).
Commit Automatically output the data automatically. Alternatively, press Commit.

Example

We will use deerwester.tab to find similar documents in this small corpus. Load the data with Corpus and pass it to Similarity Hashing. We will keep the default hash size and shingle length. We can observe what the widget outputs in a Data Table. There are 64 new attributes available, corresponding to the Simhash size parameter.

../_images/Similarity-Hashing-Example.png
References

Charikar, M. (2002) Similarity estimation techniques from rounding algorithms. STOC ‘02 Proceedings of the thirty-fourth annual ACM symposium on Theory of computing, p. 380-388.

==Referensi==

* https://orange3-text.readthedocs.io/en/latest/widgets/similarityhashing.html

==Pranala Menarik==

* [[Orange]]