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PACRR: A Position-Aware Neural IR Model for Relevance Matching

MPG-Autoren
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Hui,  Kai
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Yates,  Andrew
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Berberich,  Klaus
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Volltexte (frei zugänglich)

1704.03940
(Preprint), 382KB

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Zitation

Hui, K., Yates, A., Berberich, K., & de Melo, G. (2017). PACRR: A Position-Aware Neural IR Model for Relevance Matching. Retrieved from http://arxiv.org/abs/1704.03940.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002D-90A8-3
Zusammenfassung
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 (Position-Aware Convolutional-Recurrent Relevance), aiming at better modeling position-dependent interactions between a query and a document via convolutional layers as well as recurrent layers. Extensive experiments on six years' TREC Web Track data confirm that the proposed model yields better results under different benchmarks.