Difference between revisions of "A Self-Adaptive Explicit Semantic Analysis Method for Computing Semantic Relatedness Using Wikipedia"

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== Overview ==
 
== Overview ==
 
In recent years, the explicit semantic analysis (ESA) method has got a good performance in computing semantic [[relatedness]] (SR). However, ESA method has failed to consider the given context of the word-pair, and generates the same semantic concepts for one word in different word-pairs. It canpsilat exactly determine the intended sense of an ambiguous word. In this paper, authors propose an improved method for computing semantic relatedness. Authors technique, the self-adaptive explicit semantic analysis (SAESA), is unique in that it generates corresponding concepts to express the intended meaning for the word, according to the different words being compared and the different context. Experimental results on WordSimilarity-353 benchmark dataset show that the proposed method are superior to those of existing methods, the correlation of computed result with human judgment has an improvement from r = 0.74 to 0.81.
 
In recent years, the explicit semantic analysis (ESA) method has got a good performance in computing semantic [[relatedness]] (SR). However, ESA method has failed to consider the given context of the word-pair, and generates the same semantic concepts for one word in different word-pairs. It canpsilat exactly determine the intended sense of an ambiguous word. In this paper, authors propose an improved method for computing semantic relatedness. Authors technique, the self-adaptive explicit semantic analysis (SAESA), is unique in that it generates corresponding concepts to express the intended meaning for the word, according to the different words being compared and the different context. Experimental results on WordSimilarity-353 benchmark dataset show that the proposed method are superior to those of existing methods, the correlation of computed result with human judgment has an improvement from r = 0.74 to 0.81.
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== Embed ==
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=== Wikipedia Quality ===
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Wang, Weiping; Chen, Peng; Liu, Bowen. (2008). "[[A Self-Adaptive Explicit Semantic Analysis Method for Computing Semantic Relatedness Using Wikipedia]]".DOI: 10.1109/FITME.2008.36.
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=== English Wikipedia ===
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{{cite journal |last1=Wang |first1=Weiping |last2=Chen |first2=Peng |last3=Liu |first3=Bowen |title=A Self-Adaptive Explicit Semantic Analysis Method for Computing Semantic Relatedness Using Wikipedia |date=2008 |doi=10.1109/FITME.2008.36 |url=https://wikipediaquality.com/wiki/A_Self-Adaptive_Explicit_Semantic_Analysis_Method_for_Computing_Semantic_Relatedness_Using_Wikipedia}}
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=== HTML ===
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Wang, Weiping; Chen, Peng; Liu, Bowen. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/A_Self-Adaptive_Explicit_Semantic_Analysis_Method_for_Computing_Semantic_Relatedness_Using_Wikipedia">A Self-Adaptive Explicit Semantic Analysis Method for Computing Semantic Relatedness Using Wikipedia</a>&amp;quot;.DOI: 10.1109/FITME.2008.36.
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Revision as of 10:47, 7 March 2021


A Self-Adaptive Explicit Semantic Analysis Method for Computing Semantic Relatedness Using Wikipedia
Authors
Weiping Wang
Peng Chen
Bowen Liu
Publication date
2008
DOI
10.1109/FITME.2008.36
Links
Original

A Self-Adaptive Explicit Semantic Analysis Method for Computing Semantic Relatedness Using Wikipedia - scientific work related to Wikipedia quality published in 2008, written by Weiping Wang, Peng Chen and Bowen Liu.

Overview

In recent years, the explicit semantic analysis (ESA) method has got a good performance in computing semantic relatedness (SR). However, ESA method has failed to consider the given context of the word-pair, and generates the same semantic concepts for one word in different word-pairs. It canpsilat exactly determine the intended sense of an ambiguous word. In this paper, authors propose an improved method for computing semantic relatedness. Authors technique, the self-adaptive explicit semantic analysis (SAESA), is unique in that it generates corresponding concepts to express the intended meaning for the word, according to the different words being compared and the different context. Experimental results on WordSimilarity-353 benchmark dataset show that the proposed method are superior to those of existing methods, the correlation of computed result with human judgment has an improvement from r = 0.74 to 0.81.

Embed

Wikipedia Quality

Wang, Weiping; Chen, Peng; Liu, Bowen. (2008). "[[A Self-Adaptive Explicit Semantic Analysis Method for Computing Semantic Relatedness Using Wikipedia]]".DOI: 10.1109/FITME.2008.36.

English Wikipedia

{{cite journal |last1=Wang |first1=Weiping |last2=Chen |first2=Peng |last3=Liu |first3=Bowen |title=A Self-Adaptive Explicit Semantic Analysis Method for Computing Semantic Relatedness Using Wikipedia |date=2008 |doi=10.1109/FITME.2008.36 |url=https://wikipediaquality.com/wiki/A_Self-Adaptive_Explicit_Semantic_Analysis_Method_for_Computing_Semantic_Relatedness_Using_Wikipedia}}

HTML

Wang, Weiping; Chen, Peng; Liu, Bowen. (2008). &quot;<a href="https://wikipediaquality.com/wiki/A_Self-Adaptive_Explicit_Semantic_Analysis_Method_for_Computing_Semantic_Relatedness_Using_Wikipedia">A Self-Adaptive Explicit Semantic Analysis Method for Computing Semantic Relatedness Using Wikipedia</a>&quot;.DOI: 10.1109/FITME.2008.36.