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.

@inproceedings{strube2006wikirelate, author = {Strube, Michael and Ponzetto, Simone Paolo}, title = {WikiRelate! Computing Semantic Relatedness Using Wikipedia.}, booktitle = {AAAI}, publisher = {AAAI Press}, year = {2006}, url = {http://www.dit.unitn.it/~p2p/RelatedWork/Matching/aaai06.pdf}, keywords = {ol_web2.0, semantic_relatedness, wikipedia, wikirelate, data_wikis}, abstract = {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.} }

%0 = inproceedings %A = Strube, Michael and Ponzetto, Simone Paolo %B = AAAI %D = 2006 %I = AAAI Press %T = WikiRelate! Computing Semantic Relatedness Using Wikipedia. %U = http://www.dit.unitn.it/~p2p/RelatedWork/Matching/aaai06.pdf