Difference between revisions of "A Hybrid Model for Learning Semantic Relatedness Using Wikipedia-Based Features"

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| title = A Hybrid Model for Learning Semantic Relatedness Using Wikipedia-Based Features
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| date = 2014
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| authors = [[Shahida Jabeen]]<br />[[Xiaoying Gao]]<br />[[Peter Andreae]]
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| doi = 10.1007/978-3-319-11749-2_39
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| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-11749-2_39.pdf
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}}
 
'''A Hybrid Model for Learning Semantic Relatedness Using Wikipedia-Based Features''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Shahida Jabeen]], [[Xiaoying Gao]] and [[Peter Andreae]].
 
'''A Hybrid Model for Learning Semantic Relatedness Using Wikipedia-Based Features''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Shahida Jabeen]], [[Xiaoying Gao]] and [[Peter Andreae]].
  
 
== Overview ==
 
== Overview ==
 
Semantic [[relatedness]] computation is the task of quantifying the degree of relatedness of two concepts. The performance of existing approaches to computing semantic relatedness is highly dependent on particular aspects of relatedness. For instance, taxonomy-based approaches aim at computing similarity, which is a special case of semantic relatedness. On the other hand, corpus-based approaches focus on the associative relations of words by taking their distributional [[features]] into account. Based on the assumption that different aspects of knowledge sources cover different kinds of semantic relations, this paper presents a hybrid model for computing semantic relatedness of words using new features extracted from various aspects of [[Wikipedia]]. The focus of this paper is on finding the optimal feature combination(s) that enhance the performance of the hybrid model. The empirical evaluation on benchmark datasets has shown that hybrid features perform better than single features by providing a complementary coverage of semantic relations, leading to improved correlation with human judgments.
 
Semantic [[relatedness]] computation is the task of quantifying the degree of relatedness of two concepts. The performance of existing approaches to computing semantic relatedness is highly dependent on particular aspects of relatedness. For instance, taxonomy-based approaches aim at computing similarity, which is a special case of semantic relatedness. On the other hand, corpus-based approaches focus on the associative relations of words by taking their distributional [[features]] into account. Based on the assumption that different aspects of knowledge sources cover different kinds of semantic relations, this paper presents a hybrid model for computing semantic relatedness of words using new features extracted from various aspects of [[Wikipedia]]. The focus of this paper is on finding the optimal feature combination(s) that enhance the performance of the hybrid model. The empirical evaluation on benchmark datasets has shown that hybrid features perform better than single features by providing a complementary coverage of semantic relations, leading to improved correlation with human judgments.

Revision as of 12:09, 2 November 2020


A Hybrid Model for Learning Semantic Relatedness Using Wikipedia-Based Features
Authors
Shahida Jabeen
Xiaoying Gao
Peter Andreae
Publication date
2014
DOI
10.1007/978-3-319-11749-2_39
Links
Original

A Hybrid Model for Learning Semantic Relatedness Using Wikipedia-Based Features - scientific work related to Wikipedia quality published in 2014, written by Shahida Jabeen, Xiaoying Gao and Peter Andreae.

Overview

Semantic relatedness computation is the task of quantifying the degree of relatedness of two concepts. The performance of existing approaches to computing semantic relatedness is highly dependent on particular aspects of relatedness. For instance, taxonomy-based approaches aim at computing similarity, which is a special case of semantic relatedness. On the other hand, corpus-based approaches focus on the associative relations of words by taking their distributional features into account. Based on the assumption that different aspects of knowledge sources cover different kinds of semantic relations, this paper presents a hybrid model for computing semantic relatedness of words using new features extracted from various aspects of Wikipedia. The focus of this paper is on finding the optimal feature combination(s) that enhance the performance of the hybrid model. The empirical evaluation on benchmark datasets has shown that hybrid features perform better than single features by providing a complementary coverage of semantic relations, leading to improved correlation with human judgments.