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• W2964012472 abstract "In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR aiming at better modeling position-dependent interactions between a query and a document. Extensive experiments on six years’ TREC Web Track data confirm that the proposed model yields better results under multiple benchmarks." @default.
• W2964012472 created "2019-07-30" @default.
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• W2964012472 date "2017-01-01" @default.
• W2964012472 modified "2023-10-14" @default.
• W2964012472 title "PACRR: A Position-Aware Neural IR Model for Relevance Matching" @default.
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• W2964012472 doi "https://doi.org/10.18653/v1/d17-1110" @default.
• W2964012472 hasPublicationYear "2017" @default.
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