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Statistical feature extraction for cross-language web content quality assessment

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Shuo ShenAuthors Info & Claims

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 1129 - 1130

https://doi.org/10.1145/2009916.2010083

Published: 24 July 2011 Publication History

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Abstract

Web content quality assessment is a typical static ranking problem. Heuristic content and TFIDF features based statistical systems have proven effective for Web content quality assessment. But they are all language dependent features, which are not suitable for cross-language ranking. In this paper, we fuse a series of language-independent features including hostname features, domain registration features, two-layer hyperlink analysis features and third-party Web service features to assess the Web content quality. The experiments on ECML/PKDD 2010 Discovery Challenge cross-language datasets show that the assessment is effective.

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András B., Carlos C., Julien M., Michael M., Miklós E., Zoltán G. ECML/PKDD 2010 Discovery Challenge data set. Crawled by European Archive Foundation.

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Statistical feature extraction for cross-language web content quality assessment

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Published In

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2011

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SIGIR '11

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SIGIR

SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Statistical cross-language Web content quality assessment

Screen Content Quality Assessment: Overview, Benchmark, and Beyond

A practical approach to the assessment of quality in use of corporate web sites