Analysis of the Wikipedia Category Graph for NLP Applications
T. Zesch, and I. Gurevych. Proceedings of the TextGraphs-2 Workshop (NAACL-HLT), page 1--8. Rochester, Association for Computational Linguistics, (April 2007)
Abstract
In this paper, we discuss two graphs in Wikipedia (i) the article graph, and (ii) the category graph. We perform a graph-theoretic analysis of the category graph, and show that it is a scale-free, small world graph like other well-known lexical semantic networks. We substantiate our findings by transferring semantic relatedness algorithms defined on WordNet
to the Wikipedia category graph. To assess the usefulness of the category graph as an NLP resource, we analyze its coverage and the performance of the transferred semantic relatedness algorithms.
%0 Conference Paper
%1 zesch2007analysis
%A Zesch, Torsten
%A Gurevych, Iryna
%B Proceedings of the TextGraphs-2 Workshop (NAACL-HLT)
%C Rochester
%D 2007
%I Association for Computational Linguistics
%K analysis category graph language natural network nlp processing wikipedia
%P 1--8
%T Analysis of the Wikipedia Category Graph for NLP Applications
%U http://acl.ldc.upenn.edu/W/W07/W07-02.pdf#page=11
%X In this paper, we discuss two graphs in Wikipedia (i) the article graph, and (ii) the category graph. We perform a graph-theoretic analysis of the category graph, and show that it is a scale-free, small world graph like other well-known lexical semantic networks. We substantiate our findings by transferring semantic relatedness algorithms defined on WordNet
to the Wikipedia category graph. To assess the usefulness of the category graph as an NLP resource, we analyze its coverage and the performance of the transferred semantic relatedness algorithms.
@inproceedings{zesch2007analysis,
abstract = {In this paper, we discuss two graphs in Wikipedia (i) the article graph, and (ii) the category graph. We perform a graph-theoretic analysis of the category graph, and show that it is a scale-free, small world graph like other well-known lexical semantic networks. We substantiate our findings by transferring semantic relatedness algorithms defined on WordNet
to the Wikipedia category graph. To assess the usefulness of the category graph as an NLP resource, we analyze its coverage and the performance of the transferred semantic relatedness algorithms.
},
added-at = {2013-03-22T12:46:34.000+0100},
address = {Rochester},
author = {Zesch, Torsten and Gurevych, Iryna},
biburl = {https://www.bibsonomy.org/bibtex/2332ed720a72bf069275f93485432314b/jaeschke},
booktitle = {Proceedings of the TextGraphs-2 Workshop (NAACL-HLT)},
interhash = {0401e62edb9bfa85dd498cb40301c0cb},
intrahash = {332ed720a72bf069275f93485432314b},
keywords = {analysis category graph language natural network nlp processing wikipedia},
month = apr,
pages = {1--8},
publisher = {Association for Computational Linguistics},
timestamp = {2014-07-28T15:57:31.000+0200},
title = {Analysis of the Wikipedia Category Graph for NLP Applications},
url = {http://acl.ldc.upenn.edu/W/W07/W07-02.pdf#page=11},
year = 2007
}