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Proceedings of the 12th international conference on World Wide Web table of contents
Budapest, Hungary
SESSION: Information Retrieval table of contents
Pages: 1 - 10  
Year of Publication: 2003
ISBN:1-58113-680-3
Authors
Monika Henzinger  Google, Inc., Mountain View, CA
Bay-Wei Chang  Google, Inc., Mountain View, CA
Brian Milch  University of California, Berkeley, Berkeley, CA
Sergey Brin  Google, Inc., Mountain View, CA
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 31,   Downloads (12 Months): 153,   Citation Count: 20
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ABSTRACT

Many daily activities present information in the form of a stream of text, and often people can benefit from additional information on the topic discussed. TV broadcast news can be treated as one such stream of text; in this paper we discuss finding news articles on the web that are relevant to news currently being broadcast.We evaluated a variety of algorithms for this problem, looking at the impact of inverse document frequency, stemming, compounds, history, and query length on the relevance and coverage of news articles returned in real time during a broadcast. We also evaluated several postprocessing techniques for improving the precision, including reranking using additional terms, reranking by document similarity, and filtering on document similarity. For the best algorithm, 84%-91% of the articles found were relevant, with at least 64% of the articles being on the exact topic of the broadcast. In addition, a relevant article was found for at least 70% of the topics.


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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CITED BY  20
 
 
 

Collaborative Colleagues:
Monika Henzinger: colleagues
Bay-Wei Chang: colleagues
Brian Milch: colleagues
Sergey Brin: colleagues

Peer to Peer - Readers of this Article have also read: