Orange: Similarity Hashing - Revision history

Onnowpurbo: /* Contoh */

2020-04-12T02:54:26Z

Contoh

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2020-04-12T02:54:19Z

Contoh

Onnowpurbo at 10:51, 11 April 2020

2020-04-11T10:51:59Z

Onnowpurbo at 03:27, 29 January 2020

2020-01-29T03:27:31Z

Onnowpurbo at 08:56, 24 January 2020

2020-01-24T08:56:30Z

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]]

@@ Line 23: / Line 23: @@
 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==

@@ Line 21: / Line 21: @@
 ==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]]
 ==Referensi==

@@ Line 2: / Line 2: @@
-Computes documents hashes.
+Widget Similarity Hashing menghitung nilai hash dokumen.
 ==Input==
@@ Line 12: / Line 12: @@
   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).
@@ Line 21: / Line 21: @@
 ==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]]