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An unsupervised semantic similarity based method for word sense disambiguation

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dc.contributor Graduate Program in Management Information Systems.
dc.contributor.advisor Kutlu, Birgül.
dc.contributor.author Çankaya, Sedat.
dc.date.accessioned 2023-03-16T12:51:42Z
dc.date.available 2023-03-16T12:51:42Z
dc.date.issued 2010.
dc.identifier.other MIS 2010 C36
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/18144
dc.description.abstract In this thesis, a semantic similarity based unsupervised method for word sense disambiguation is presented. The method tries to disambiguate a target word by calculating a similarity score between the words surrounding the target word and the words existing in the sense definition of the target word. The built-in semantic hierarchy and synset relations of WordNet, a machine readable thesauri, are used in similarity score calculations. The method is evaluated using SemCor data and the results are compared against other methods based on semantic similarity and unsupervised methods. Results show us that increasing the number of inputs by including the words in a word’s sense into disambiguation process, improves precision rate of disambiguation process.
dc.format.extent 30cm.
dc.publisher Thesis (M.A.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2010.
dc.relation Includes appendices.
dc.relation Includes appendices.
dc.subject.lcsh Semantics -- Data processing.
dc.subject.lcsh Natural language processing (Computer science)
dc.subject.lcsh Computational linguistics.
dc.title An unsupervised semantic similarity based method for word sense disambiguation
dc.format.pages v, 54 leaves;


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