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

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


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
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1049–1058
Language:
URL:
https://www.aclweb.org/anthology/D17-1110
DOI:
10.18653/v1/D17-1110
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1110.pdf
Video:
 https://vimeo.com/238235171