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dc.contributor.advisorHuang, Xiangji
dc.creatorWu, Jiajin
dc.date.accessioned2014-07-31T16:56:33Z
dc.date.available2014-07-31T16:56:33Z
dc.date.copyright2014-04-24
dc.date.issued2014-07-28
dc.identifier.urihttp://hdl.handle.net/10315/27704
dc.description.abstractIn most of the traditional information retrieval (IR) models, the independent relevance assumption is taken, which assumes the relevance of a document is independent of other documents. However, the pitfall of this is the high redundancy and low diversity of retrieval result. This has been seen in many scenarios, especially in biomedical IR, where the information need of one query may refer to different aspects. Promoting diversity in IR takes the relationship between documents into account. Unlike previous studies, we tackle this problem in the learning to rank perspective. The main challenges are how to find salient features for biomedical data and how to integrate dynamic features into the ranking model. To address these challenges, Wikipedia is used to detect topics of documents for generating diversity biased features. A combined model is proposed and studied to learn a diversified ranking result. Experiment results show the proposed method outperforms baseline models.en_US
dc.language.isoenen_US
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectInformation technologyen_US
dc.subjectComputer scienceen_US
dc.titleUsing Learning to Rank Approach to Promoting Diversity for Biomedical Information Retrieval with Wikipediaen_US
dc.typeElectronic Thesis or Dissertation
dc.degree.disciplineInformation Systems and Technology
dc.degree.nameMA - Master of Arts
dc.degree.levelMaster's
dc.date.updated2014-07-28T16:24:23Z
dc.subject.keywordsWikipediaen_US
dc.subject.keywordsBiomedical Information Retrievalen_US
dc.subject.keywordsLearning to Ranken_US
dc.subject.keywordsDiversity Information Retrievalen_US


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