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Leveraging information from relevance assessments has been proposed as an effective means for improving retrieval. The authors introduce a novel language modeling method which uses information from each assessed document and their aggregate. While most previous approaches focus either on features of the entire set or on features of the individual relevant documents, their model exploits features of both the documents and the set as a whole. When evaluated, they show that their model is able to significantly improve over state-of-art feedback methods.
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