@inproceedings{li-etal-2018-nprf,
title = "{NPRF}: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval",
author = "Li, Canjia and
Sun, Yingfei and
He, Ben and
Wang, Le and
Hui, Kai and
Yates, Andrew and
Sun, Le and
Xu, Jungang",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1478",
doi = "10.18653/v1/D18-1478",
pages = "4482--4491",
abstract = "Pseudo relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches. While neural retrieval models have recently demonstrated strong results for ad-hoc retrieval, combining them with PRF is not straightforward due to incompatibilities between existing PRF approaches and neural architectures. To bridge this gap, we propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. Extensive experiments on two standard test collections confirm the effectiveness of the proposed NPRF framework in improving the performance of two state-of-the-art neural IR models.",
}
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<abstract>Pseudo relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches. While neural retrieval models have recently demonstrated strong results for ad-hoc retrieval, combining them with PRF is not straightforward due to incompatibilities between existing PRF approaches and neural architectures. To bridge this gap, we propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. Extensive experiments on two standard test collections confirm the effectiveness of the proposed NPRF framework in improving the performance of two state-of-the-art neural IR models.</abstract>
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%0 Conference Proceedings
%T NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
%A Li, Canjia
%A Sun, Yingfei
%A He, Ben
%A Wang, Le
%A Hui, Kai
%A Yates, Andrew
%A Sun, Le
%A Xu, Jungang
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F li-etal-2018-nprf
%X Pseudo relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches. While neural retrieval models have recently demonstrated strong results for ad-hoc retrieval, combining them with PRF is not straightforward due to incompatibilities between existing PRF approaches and neural architectures. To bridge this gap, we propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. Extensive experiments on two standard test collections confirm the effectiveness of the proposed NPRF framework in improving the performance of two state-of-the-art neural IR models.
%R 10.18653/v1/D18-1478
%U https://aclanthology.org/D18-1478
%U https://doi.org/10.18653/v1/D18-1478
%P 4482-4491
Markdown (Informal)
[NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval](https://aclanthology.org/D18-1478) (Li et al., EMNLP 2018)
ACL
- Canjia Li, Yingfei Sun, Ben He, Le Wang, Kai Hui, Andrew Yates, Le Sun, and Jungang Xu. 2018. NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4482–4491, Brussels, Belgium. Association for Computational Linguistics.