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dc.contributor.advisorJulie A. Shah.en_US
dc.contributor.authorZhou, Yilun,S.M.Massachusetts Institute of Technology.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2019-11-04T20:21:30Z
dc.date.available2019-11-04T20:21:30Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/122731
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 49-52).en_US
dc.description.abstractIn many applications, it is important to characterize the way in which two concepts are semantically related. Knowledge graphs such as ConceptNet provide a rich source of information for such characterizations by encoding relations between concepts as edges in a graph. When two concepts are not directly connected by an edge, their relationship can still be described in terms of the paths that connect them. Unfortunately, many of these paths are uninformative and noisy, meaning that the success of applications that use such path features crucially relies on their ability to select high-quality paths. In existing applications, this path selection process is based on relatively simple heuristics. In this thesis I instead propose to learn to predict path quality from crowdsourced human assessments. Since a generic task-independent notion of quality is concerned, human participants are asked to rank paths according to their subjective assessment of the paths' naturalness, without being given specific definitions or guidelines. Experiments show that a neural network model trained on these assessments is able to predict human judgments on unseen paths with near optimal performance. Most notably, the resulting path selection method is substantially better than the current heuristic approaches at identifying meaningful paths in various applications.en_US
dc.description.statementofresponsibilityby Yilun Zhou.en_US
dc.format.extent52 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleData-driven path filtering in ConceptNeten_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1124679389en_US
dc.description.collectionS.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2019-11-04T20:21:29Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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