Abstract/Details

Retrieval performance prediction and document quality


2008 2008

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Abstract (summary)

The ability to predict retrieval performance has potential applications in many important IR (Information Retrieval) areas. In this thesis, we study the problem of predicting retrieval quality at the granularity of both the retrieved document set as a whole and individual retrieved documents. At the level of ranked lists of documents, we propose several novel prediction models that capture different aspects of the retrieval process that have a major impact on retrieval effectiveness. These techniques make performance prediction both effective and efficient in various retrieval settings including a Web search environment. As an application, we also provide a framework to address the problem of query expansion prediction. At the level of documents, we predict the quality of documents in the context of Web ad-hoc retrieval. We explore document features that are predictive of quality. Furthermore, we propose a document quality language model to improve retrieval effectiveness by incorporating quality information.

Indexing (details)


Subject
Computer science
Classification
0984: Computer science
Identifier / keyword
Applied sciences; Document quality; Information retrieval; Retrieval performance
Title
Retrieval performance prediction and document quality
Author
Zhou, Yun
Number of pages
149
Publication year
2008
Degree date
2008
School code
0118
Source
DAI-B 69/08, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780549663539
Advisor
Croft, William
University/institution
University of Massachusetts Amherst
Department
Computer Science
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3315475
ProQuest document ID
230683616
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/230683616
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