Difference between revisions of "Wikipedia based Semantic Related Chinese Words Exploring and Relatedness Computing"

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== Overview ==
 
== Overview ==
 
To find how to collect semantic related words and calculate semantic [[relatedness]],an experiment is done to download about 50 thousand documents from the web site of [[Chinese Wikipedia]] and extract hyperlinks between lines which contains [[semantic information]].By mining hyperlinked references in documents,about 400 thousand semantic related word pairs are collected.With more experiments on topic groups of related words,tightly related words are grouped into smaller sets with an average semantic relatedness calculated.Semantic relatedness is calculated using information of hyperlink positions and frequencies in documents.Comparing with the result by classic algorithms,the [[reliability]] of the new [[measures]] is analyzed.
 
To find how to collect semantic related words and calculate semantic [[relatedness]],an experiment is done to download about 50 thousand documents from the web site of [[Chinese Wikipedia]] and extract hyperlinks between lines which contains [[semantic information]].By mining hyperlinked references in documents,about 400 thousand semantic related word pairs are collected.With more experiments on topic groups of related words,tightly related words are grouped into smaller sets with an average semantic relatedness calculated.Semantic relatedness is calculated using information of hyperlink positions and frequencies in documents.Comparing with the result by classic algorithms,the [[reliability]] of the new [[measures]] is analyzed.
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== Embed ==
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=== Wikipedia Quality ===
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Yi-xin, Zhong. (2009). "[[Wikipedia based Semantic Related Chinese Words Exploring and Relatedness Computing]]".
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=== English Wikipedia ===
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{{cite journal |last1=Yi-xin |first1=Zhong |title=Wikipedia based Semantic Related Chinese Words Exploring and Relatedness Computing |date=2009 |url=https://wikipediaquality.com/wiki/Wikipedia_based_Semantic_Related_Chinese_Words_Exploring_and_Relatedness_Computing}}
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=== HTML ===
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Yi-xin, Zhong. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_based_Semantic_Related_Chinese_Words_Exploring_and_Relatedness_Computing">Wikipedia based Semantic Related Chinese Words Exploring and Relatedness Computing</a>&amp;quot;.
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Revision as of 12:19, 15 September 2019


Wikipedia based Semantic Related Chinese Words Exploring and Relatedness Computing
Authors
Zhong Yi-xin
Publication date
2009
Links
Original

Wikipedia based Semantic Related Chinese Words Exploring and Relatedness Computing - scientific work related to Wikipedia quality published in 2009, written by Zhong Yi-xin.

Overview

To find how to collect semantic related words and calculate semantic relatedness,an experiment is done to download about 50 thousand documents from the web site of Chinese Wikipedia and extract hyperlinks between lines which contains semantic information.By mining hyperlinked references in documents,about 400 thousand semantic related word pairs are collected.With more experiments on topic groups of related words,tightly related words are grouped into smaller sets with an average semantic relatedness calculated.Semantic relatedness is calculated using information of hyperlink positions and frequencies in documents.Comparing with the result by classic algorithms,the reliability of the new measures is analyzed.

Embed

Wikipedia Quality

Yi-xin, Zhong. (2009). "[[Wikipedia based Semantic Related Chinese Words Exploring and Relatedness Computing]]".

English Wikipedia

{{cite journal |last1=Yi-xin |first1=Zhong |title=Wikipedia based Semantic Related Chinese Words Exploring and Relatedness Computing |date=2009 |url=https://wikipediaquality.com/wiki/Wikipedia_based_Semantic_Related_Chinese_Words_Exploring_and_Relatedness_Computing}}

HTML

Yi-xin, Zhong. (2009). &quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_based_Semantic_Related_Chinese_Words_Exploring_and_Relatedness_Computing">Wikipedia based Semantic Related Chinese Words Exploring and Relatedness Computing</a>&quot;.