Difference between revisions of "Knowledge Derived from Wikipedia for Computing Semantic Relatedness"
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| − | '''Knowledge Derived from Wikipedia for Computing Semantic Relatedness''' - scientific work related to Wikipedia quality published in 2007, written by Simone Paolo Ponzetto and Michael Strube. | + | '''Knowledge Derived from Wikipedia for Computing Semantic Relatedness''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Simone Paolo Ponzetto]] and [[Michael Strube]]. |
== Overview == | == Overview == | ||
| − | Wikipedia provides a semantic network for computing semantic relatedness in a more structured fashion than a search engine and with more coverage than WordNet. Authors 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 authors show that Wikipedia outperforms WordNet on some datasets. Authors also address the question whether and how Wikipedia can be integrated into NLP applications as a knowledge base. Including Wikipedia improves the performance of a machine learning based coreference resolution system, indicating that it represents a valuable resource for NLP applications. Finally, authors show that method can be easily used for languages other than English by computing semantic relatedness for a German dataset. | + | Wikipedia provides a semantic network for computing semantic [[relatedness]] in a more structured fashion than a search engine and with more coverage than [[WordNet]]. Authors 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 authors show that Wikipedia outperforms WordNet on some datasets. Authors also address the question whether and how Wikipedia can be integrated into NLP applications as a knowledge base. Including Wikipedia improves the performance of a machine learning based coreference resolution system, indicating that it represents a valuable resource for NLP applications. Finally, authors show that method can be easily used for languages other than English by computing semantic relatedness for a German dataset. |
Revision as of 08:59, 10 October 2019
Knowledge Derived from Wikipedia for Computing Semantic Relatedness - scientific work related to Wikipedia quality published in 2007, written by Simone Paolo Ponzetto and Michael Strube.
Overview
Wikipedia provides a semantic network for computing semantic relatedness in a more structured fashion than a search engine and with more coverage than WordNet. Authors 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 authors show that Wikipedia outperforms WordNet on some datasets. Authors also address the question whether and how Wikipedia can be integrated into NLP applications as a knowledge base. Including Wikipedia improves the performance of a machine learning based coreference resolution system, indicating that it represents a valuable resource for NLP applications. Finally, authors show that method can be easily used for languages other than English by computing semantic relatedness for a German dataset.
