PUMA publications for /tag/wikirelatehttps://puma.uni-kassel.de/tag/wikirelatePUMA RSS feed for /tag/wikirelate2024-03-28T20:09:09+01:00WikiRelate! Computing Semantic Relatedness Using Wikipedia.https://puma.uni-kassel.de/bibtex/29216a46b593c3319aa23d13ca8373beb/benzbenz2011-02-04T16:09:24+01:00ol_web2.0 semantic_relatedness wikipedia wikirelate data_wikis <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Michael Strube" itemprop="url" href="/author/Michael%20Strube"><span itemprop="name">M. Strube</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Simone Paolo Ponzetto" itemprop="url" href="/author/Simone%20Paolo%20Ponzetto"><span itemprop="name">S. Ponzetto</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">AAAI</span>, </em></span><em><span itemprop="publisher">AAAI Press</span>, </em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Fri Feb 04 16:09:24 CET 2011AAAIconf/aaai/2006WikiRelate! Computing Semantic Relatedness Using Wikipedia.2006ol_web2.0 semantic_relatedness wikipedia wikirelate data_wikis Wikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose. The best results on this dataset are obtained by integrating Google, WordNet and Wikipedia based measures. We also show that including Wikipedia improves the performance of an NLP application processing naturally occurring texts.dblp