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dc.contributor.advisorGoldwater, Sharon
dc.contributor.advisorDeoskar, Tejaswini
dc.contributor.authorKosinowski, Hanne
dc.date.accessioned2014-03-20T12:18:34Z
dc.date.available2014-03-20T12:18:34Z
dc.date.issued2012-11-28
dc.identifier.urihttp://hdl.handle.net/1842/8470
dc.description.abstractLanguage changes constantly – new words are created on a daily basis. This thesis examines blends in English, a highly productive word formation process where two words are combined to form a new word with a new meaning. In order to allow natural language processing system to handle blends, I present a system that automatically extracts the words comprising the blend using a set of statistical features. Using the features on a corpus consisting of 2236 blends and a logistic regression classifier, I obtain a 50% accuracy on the gold standard. So far, this is the largest corpus of blends used for this task. I compare the results to previous work and provide solutions on how to improve the system’s performance.en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.subjectLanguage Processingen
dc.subjectBlendsen
dc.titleAutomatically Extracting the Source Words of English Lexical Blendsen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelMastersen
dc.type.qualificationnameMSc Master of Scienceen
dcterms.accessRightsRestricted Accessen_US


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