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- W2018478052 abstract "We present a flexible new optimization framework for finding effective, reliable pseudo-relevance feedback models that unifies existing complementary approaches in a principled way. The result is an algorithmic approach that not only brings together different benefits of previous methods, such as parameter self-tuning and risk reduction from term dependency modeling, but also allows a rich new space of model search strategies to be investigated. We compare the effectiveness of a unified algorithm to existing methods by examining iterative performance and risk-reward tradeoffs. We also discuss extensions for generating new algorithms within our framework." @default.
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- W2018478052 date "2010-10-26" @default.
- W2018478052 modified "2023-10-16" @default.
- W2018478052 title "A unified optimization framework for robust pseudo-relevance feedback algorithms" @default.
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- W2018478052 doi "https://doi.org/10.1145/1871437.1871573" @default.
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