Building an Intelligent Filtering System Using Idea Indexing

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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 ... continued below

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Yang, Li August 2003.

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This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 283 times . More information about this thesis can be viewed below.

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  • Yang, Li

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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.

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  • August 2003

Added to The UNT Digital Library

  • Feb. 15, 2008, 2:53 p.m.

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  • May 6, 2014, 4:17 p.m.

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Yang, Li. Building an Intelligent Filtering System Using Idea Indexing, thesis, August 2003; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc4275/: accessed December 12, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .