Rewarding the Location of Terms in Sentences to Enhance Probabilistic Information Retrieval

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dc.contributor.advisorHuang, Xiangji
dc.creatorLiu, Baiyan
dc.date.accessioned2017-07-27T13:38:03Z
dc.date.available2017-07-27T13:38:03Z
dc.date.copyright2016-09-23
dc.date.issued2017-07-27
dc.date.updated2017-07-27T13:38:03Z
dc.degree.disciplineInformation Systems and Technology
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractIn most traditional retrieval models, the weight (or probability) of a query term is estimated based on its own distribution or statistics. Intuitively, however, the nouns are more important in information retrieval and are more often found near the beginning and the end of sentences. In this thesis, we investigate the effect of rewarding the terms based on their location in sentences on information retrieval. Particularly, we propose a kernel-based method to capture the term placement pattern, in which a novel Term Location retrieval model is derived in combination with the BM25 model to enhance probabilistic information retrieval. Experiments on five TREC datasets of varied size and content indicates that the proposed model significantly outperforms the optimized BM25 and DirichletLM in MAP over all datasets with all kernel functions, and excels compared to the optimized BM25 and DirichletLM over most of the datasets in P@5 and P@20 with different kernel functions.
dc.identifier.urihttp://hdl.handle.net/10315/33533
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsProbabilistic information retrieval
dc.subject.keywordsSearch
dc.subject.keywordsTerm location
dc.subject.keywordsNoun
dc.titleRewarding the Location of Terms in Sentences to Enhance Probabilistic Information Retrieval
dc.typeElectronic Thesis or Dissertation

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