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dc.contributor.advisorTrotman, Andrew
dc.contributor.authorWood, Vaughn
dc.date.available2013-03-21T03:23:00Z
dc.date.copyright2013
dc.identifier.citationWood, V. (2013). Improving Query Term Expansion With Machine Learning (Thesis, Master of Science). University of Otago. Retrieved from http://hdl.handle.net/10523/3791en
dc.identifier.urihttp://hdl.handle.net/10523/3791
dc.description.abstractVocabulary mismatch is an impediment to responding to user queries with relevant results. Stemmers solve this problem by conflating terms with similar spellings. In this thesis we use machine learning to create a stemmer optimised for Information Retrieval performance. We investigate further improvement to stemmers with corpus information. With the goal of stemming selectively for further performance gains we investigate the prediction of query performance.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Otago
dc.rightsAll items in OUR Archive are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectInformation Retrieval
dc.subjectSearch
dc.subjectSearch Engines
dc.subjectMachine Learning
dc.subjectGenetic Algorithms
dc.titleImproving Query Term Expansion With Machine Learning
dc.typeThesis
dc.date.updated2013-03-21T01:41:20Z
dc.language.rfc3066en
thesis.degree.disciplineComputer Science
thesis.degree.nameMaster of Science
thesis.degree.grantorUniversity of Otago
thesis.degree.levelMasters
otago.openaccessOpen
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