Difference between revisions of "A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia"
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| + | {{Infobox work | ||
| + | | title = A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia | ||
| + | | date = 2010 | ||
| + | | authors = [[Ziqi Zhang]]<br />[[Anna Lisa Gentile]]<br />[[Lei Xia]]<br />[[José Iria]]<br />[[Sam Chapman]] | ||
| + | | link = http://www.lrec-conf.org/proceedings/lrec2010/pdf/292_Paper.pdf | ||
| + | }} | ||
'''A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Ziqi Zhang]], [[Anna Lisa Gentile]], [[Lei Xia]], [[José Iria]] and [[Sam Chapman]]. | '''A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Ziqi Zhang]], [[Anna Lisa Gentile]], [[Lei Xia]], [[José Iria]] and [[Sam Chapman]]. | ||
== Overview == | == Overview == | ||
Determining semantic [[relatedness]] between words or concepts is a fundamental process to many [[Natural Language Processing]] applications. Approaches for this task typically make use of knowledge resources such as [[WordNet]] and [[Wikipedia]]. However, these approaches only make use of limited number of [[features]] extracted from these resources, without investigating the usefulness of combining various different features and their importance in the task of semantic relatedness. In this paper, authors propose a random walk model based approach to measuring semantic relatedness between words or concepts, which seamlessly integrates various features extracted from Wikipedia to compute semantic relatedness. Authors empirically study the usefulness of these features in the task, and prove that by combining multiple features that are weighed according to their importance, system obtains competitive results, and outperforms other systems on some datasets. | Determining semantic [[relatedness]] between words or concepts is a fundamental process to many [[Natural Language Processing]] applications. Approaches for this task typically make use of knowledge resources such as [[WordNet]] and [[Wikipedia]]. However, these approaches only make use of limited number of [[features]] extracted from these resources, without investigating the usefulness of combining various different features and their importance in the task of semantic relatedness. In this paper, authors propose a random walk model based approach to measuring semantic relatedness between words or concepts, which seamlessly integrates various features extracted from Wikipedia to compute semantic relatedness. Authors empirically study the usefulness of these features in the task, and prove that by combining multiple features that are weighed according to their importance, system obtains competitive results, and outperforms other systems on some datasets. | ||
Revision as of 14:16, 22 December 2019
Authors | Ziqi Zhang Anna Lisa Gentile Lei Xia José Iria Sam Chapman |
|---|---|
Publication date | 2010 |
Links | Original |
A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia - scientific work related to Wikipedia quality published in 2010, written by Ziqi Zhang, Anna Lisa Gentile, Lei Xia, José Iria and Sam Chapman.
Overview
Determining semantic relatedness between words or concepts is a fundamental process to many Natural Language Processing applications. Approaches for this task typically make use of knowledge resources such as WordNet and Wikipedia. However, these approaches only make use of limited number of features extracted from these resources, without investigating the usefulness of combining various different features and their importance in the task of semantic relatedness. In this paper, authors propose a random walk model based approach to measuring semantic relatedness between words or concepts, which seamlessly integrates various features extracted from Wikipedia to compute semantic relatedness. Authors empirically study the usefulness of these features in the task, and prove that by combining multiple features that are weighed according to their importance, system obtains competitive results, and outperforms other systems on some datasets.
