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SIGIR
2011
ACM
12 years 10 months ago
A boosting approach to improving pseudo-relevance feedback
Pseudo-relevance feedback has proven effective for improving the average retrieval performance. Unfortunately, many experiments have shown that although pseudo-relevance feedback...
Yuanhua Lv, ChengXiang Zhai, Wan Chen
SIGIR
2010
ACM
13 years 11 months ago
Positional relevance model for pseudo-relevance feedback
Pseudo-relevance feedback is an effective technique for improving retrieval results. Traditional feedback algorithms use a whole feedback document as a unit to extract words for ...
Yuanhua Lv, ChengXiang Zhai
MM
2005
ACM
146views Multimedia» more  MM 2005»
14 years 1 months ago
Unsupervised content discovery in composite audio
Automatically extracting semantic content from audio streams can be helpful in many multimedia applications. Motivated by the known limitations of traditional supervised approache...
Rui Cai, Lie Lu, Alan Hanjalic
CIKM
2007
Springer
14 years 1 months ago
Developing learning strategies for topic-based summarization
Most up-to-date well-behaved topic-based summarization systems are built upon the extractive framework. They score the sentences based on the associated features by manually assig...
Ouyang You, Sujian Li, Wenjie Li
MM
2006
ACM
217views Multimedia» more  MM 2006»
14 years 1 months ago
IGroup: a web image search engine with semantic clustering of search results
In this demo, we present IGroup, a Web image search engine that organizes the search results into semantic clusters. Different from all existing Web image search results clusterin...
Feng Jing, Changhu Wang, Yuhuan Yao, Kefeng Deng, ...