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Evaluation of filtering current news search results
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Source Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval table of contents
Sheffield, United Kingdom
POSTER SESSION: Posters table of contents
Pages: 494 - 495  
Year of Publication: 2004
ISBN:1-58113-881-4
Authors
Steven M. Beitzel  Illinois Institute of Technology, Chicago, IL
Eric C. Jensen  Illinois Institute of Technology, Chicago, IL
Abdur Chowdhury  Illinois Institute of Technology, Chicago, IL
David Grossman  Illinois Institute of Technology, Chicago, IL
Ophir Frieder  Illinois Institute of Technology, Chicago, IL
Sponsors
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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ABSTRACT

We describe an evaluation of result set filtering techniques for providing ultra-high precision in the task of presenting related news for general web queries. In this task, the negative user experience generated by retrieving non-relevant documents has a much worse impact than not retrieving relevant ones. We adapt cost-based metrics from the document filtering domain to this result filtering problem in order to explicitly examine the tradeoff between missing relevant documents and retrieving non-relevant ones. A large manual evaluation of three simple threshold filters shows that the basic approach of counting matching title terms outperforms also incorporating selected abstract terms based on part-of-speech or higher-level linguistic structures. Simultaneously, leveraging these cost-based metrics allows us to explicitly determine what other tasks would benefit from these alternative techniques.




Collaborative Colleagues:
Steven M. Beitzel: colleagues
Eric C. Jensen: colleagues
Abdur Chowdhury: colleagues
David Grossman: colleagues
Ophir Frieder: colleagues