Why web-based pseudo relevance feedback systems fail

Zhang, Jing, Ong, Kok-Leong and Lee, Vincent C. S. 2012, Why web-based pseudo relevance feedback systems fail, in KICSS 2012 : Proceedings : Seventh International Conference on Knowledge, Information and Creativity Support Systems, IEEE Computer Society, Piscataway, N. J., pp. 216-222.

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Title Why web-based pseudo relevance feedback systems fail
Author(s) Zhang, Jing
Ong, Kok-Leong
Lee, Vincent C. S.
Conference name Knowledge, information and creativity support systems. Conference (7th : 2012 : Melbourne, Vic.)
Conference location Melbourne, Vic
Conference dates 8-10 Nov. 2012
Title of proceedings KICSS 2012 : Proceedings : Seventh International Conference on Knowledge, Information and Creativity Support Systems
Editor(s) [unknown]
Publication date 2012
Conference series Knowledge, information and creativity support systems
Start page 216
End page 222
Total pages 7
Publisher IEEE Computer Society
Place of publication Piscataway, N. J.
Keyword(s) pseudo-relevance feedback
twitter
topic detection
Summary  We review pseudo-relevance feedback as a mechanism for expanding short texts. Where short texts exhibit evolving concepts, topics and other characteristics, Web-based feedback systems were touted as the most ideal way of enriching the feature space of short texts. However, we note from a recent implementation of a Web-based pseudo-relevance feedback that it would only perform well under clinical situations. Further improvements to address fundamental noise in Web documents did not show significant improvements leading us to conclude that relevance feedback using Web documents directly are unsuitable for real-world conditions. In this paper, we present Eddi, which is a recent system that provides an exemplar of a typical pseudo-relevance feedback system. We first show the conditions in which Eddi will work and then discuss the situations where it would fail. We then present the variations to Eddi from our attempt to improve the robustness of Eddi's algorithm when dealing with complex Web documents. We then present the results from all variations to show the lack of robustness for pseudo-relevance feedback with Web documents.
ISBN 9781467345644
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
080107 Natural Language Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30050310

Document type: Conference Paper
Collection: School of Information Technology
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Created: Fri, 25 Jan 2013, 11:13:22 EST by Kok-Leong Ong

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