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

Kai Hui, Andrew Yates, Klaus Berberich, Gerard de Melo

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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.
Anthology ID:
D17-1110
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Martha Palmer, Rebecca Hwa, Sebastian Riedel
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1049–1058
Language:
URL:
https://aclanthology.org/D17-1110
DOI:
10.18653/v1/D17-1110
Bibkey:
Cite (ACL):
Kai Hui, Andrew Yates, Klaus Berberich, and Gerard de Melo. 2017. PACRR: A Position-Aware Neural IR Model for Relevance Matching. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1049–1058, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
PACRR: A Position-Aware Neural IR Model for Relevance Matching (Hui et al., EMNLP 2017)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/D17-1110.pdf
Video:
 https://preview.aclanthology.org/teach-a-man-to-fish/D17-1110.mp4
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