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dc.contributor.authorKhandpur, Rupinder Paulen_US
dc.date.accessioned2018-08-18T08:00:21Z
dc.date.available2018-08-18T08:00:21Z
dc.date.issued2018-08-17
dc.identifier.othervt_gsexam:16706en_US
dc.identifier.urihttp://hdl.handle.net/10919/84852
dc.description.abstractDynamic query expansion is a method of automatically identifying terms relevant to a target domain based on an incomplete query input. With the explosive growth of online media, such tools are essential for efficient search result refining to track emerging themes in noisy, unstructured text streams. It's crucial for large-scale predictive analytics and decision-making, systems which use open source indicators to find meaningful information rapidly and accurately. The problems of information overload and semantic mismatch are systemic during the Information Retrieval (IR) tasks undertaken by such systems. In this dissertation, we develop approaches to dynamic query expansion algorithms that can help improve the efficacy of such systems using only a small set of seed queries and requires no training or labeled samples. We primarily investigate four significant problems related to the retrieval and assessment of event-related information, viz. (1) How can we adapt the query expansion process to support rank-based analysis when tracking a fixed set of entities? A scalable framework is essential to allow relative assessment of emerging themes such as airport threats. (2) What visual knowledge discovery framework to adopt that can incorporate users' feedback back into the search result refinement process? A crucial step to efficiently integrate real-time `situational awareness' when monitoring specific themes using open source indicators. (3) How can we contextualize query expansions? We focus on capturing semantic relatedness between a query and reference text so that it can quickly adapt to different target domains. (4) How can we synchronously perform knowledge discovery and characterization (unstructured to structured) during the retrieval process? We mainly aim to model high-order, relational aspects of event-related information from microblog texts.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectDynamic Query Expansionen_US
dc.subjectMicroblog Event Retrievalen_US
dc.subjectSocial Media Analyticsen_US
dc.subjectVisual Knowledge Discoveryen_US
dc.titleAugmenting Dynamic Query Expansion in Microblog Textsen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairRamakrishnan, Narendranen_US
dc.contributor.committeechairLu, Chang Tienen_US
dc.contributor.committeememberHan, Eui-Hongen_US
dc.contributor.committeememberNorth, Christopher L.en_US
dc.contributor.committeememberReddy, Chandan K.en_US


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