Abstract
Pseudo-relevance feedback is a popular and widely accepted query reformulation strategy for document retrieval and re-ranking. However, problems arise in this task when assumed-to-be relevant documents are actually irrelevant which causes a drift in the focus of the reformulated query. This paper focuses on news story retrieval and re-ranking, and offers a new perspective through the exploration of the pair-wise constraints derived from video near-duplicates for constraint-driven re-ranking. We propose a novel application of PageRank, which is a pseudo-relevance feedback algorithm, and use the constraints built on top of text to improve the relevance quality. Real-time experiments were conducted using a large-scale broadcast video database that contains more than 34,000 news stories.
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Wu, X., Ide, I., Satoh, S. (2010). PageRank with Text Similarity and Video Near-Duplicate Constraints for News Story Re-ranking. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_53
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DOI: https://doi.org/10.1007/978-3-642-11301-7_53
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