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Building an Intelligent Filtering System Using Idea Indexing

The widely used vector model maintains its popularity because of its simplicity, fast speed, and the appeal of using spatial proximity for semantic proximity. However, this model faces a disadvantage that is associated with the vagueness from keywords overlapping. Efforts have been made to improve the vector model. The research on improving document representation has been focused on four areas, namely, statistical co-occurrence of related items, forming term phrases, grouping of related words, and representing the content of documents. In this thesis, we propose the idea-indexing model to improve document representation for the filtering task in IR. The idea-indexing model matches document terms with the ideas they express and indexes the document with these ideas. This indexing scheme represents the document with its semantics instead of sets of independent terms. We show in this thesis that indexing with ideas leads to better performance.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc4275
Date08 1900
CreatorsYang, Li
ContributorsMihalcea, Rada, 1974-, Swigger, Kathleen M., Brazile, Robert
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
FormatText
RightsPublic, Copyright, Yang, Li, Copyright is held by the author, unless otherwise noted. All rights reserved.

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