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Document relevance assessment via term distribution analysis using fourier series expansion
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International Conference on Digital Libraries archive
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries table of contents
Austin, TX, USA
SESSION: 10 table of contents
Pages 277-284  
Year of Publication: 2009
ISBN:978-1-60558-322-8
Authors
Patricio Galeas  University of Marburg, Marburg, Germany
Ralph Kretschmer  Kretschmer Software, Siegen, Germany
Bernd Freisleben  University of Marburg, Marburg, Germany
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

In addition to the frequency of terms in a document collection, the distribution of terms plays an important role in determining the relevance of documents for a given search query. In this paper, term distribution analysis using Fourier series expansion as a novel approach for calculating an abstract representation of term positions in a document corpus is introduced. Based on this approach, two methods for improving the evaluation of document relevance are proposed: (a) a function-based ranking optimization representing a user defined document region, and (b) a query expansion technique based on overlapping the term distributions in the top-ranked documents. Experimental results demonstrate the effectiveness of the proposed approach in providing new possibilities for optimizing the retrieval process.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Patricio Galeas: colleagues
Ralph Kretschmer: colleagues
Bernd Freisleben: colleagues