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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorMoortgat, Michael
dc.contributor.authorMedema, Jonathan
dc.date.accessioned2009-02-11T15:42:19Z
dc.date.available2009-02-11T15:42:19Z
dc.date.issued2008
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/2230
dc.description.abstractIn this thesis the subject of reliable normalization in Information Extraction is discussed, specifically for the normalization of extracted items out of people's resumees. A meta-classifier approach is presented, which is based on the Memory Based Learning (MBL) implementation of the k-Nearest Neigbor Algorithm. To investigate whether this approach is a practical solution to reliable normalization a literature study is done on the subject and various experiments on domain-specific data sets were conducted. It is shown that the meta-classifier approach in combination with the MBL algorithm is one of the fastest implementations in comparison to other reliability measures and classifier algorithms. The meta-classifier is able to reach an F2-score of 86.1% on randomly created test sets.
dc.description.sponsorshipUtrecht University
dc.language.isoen
dc.titleReliable normalization in resume information extraction
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsInformation Extraction
dc.subject.keywordsReliability
dc.subject.keywordsMemory Based Learning
dc.subject.courseuuTaal- en spraaktechnologie


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