NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval

By Canjia Li, Yingfei Sun, Ben He, Le Wang, Kai Hui, Andrew Yates, Jungang Xu. 2018

74 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research, Information Retrieval & Search

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Citations in the corpus

Pretrained Transformers for Text Ranking: BERT and Beyond (2020, methodology): “The NPRF [Li et al., 2018] framework incorporates feedback documents by using a neural ranking method, like KNRM, to predict their similarity to a target document being ranked.”
A Deep Look into Neural Ranking Models for Information Retrieval (2019, background): “However, the PRF technique can also be leveraged to enhance neural ranking models (e.g., SNRM+PRF [28] and NPRF+DRMM [119] in Table 1), while human designed LTR features can be integrated into neural ranking models [33, 31] to improve the ranking performance.”
Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval (2019, methodology): “To provide two recent examples, McDonald et al. (2018) report a best AP score of 0.272 and Li et al. (2018) 0.290, compared to a simple bag-of-words query expansion baseline that achieves 0.299 (Lin, 2018).”
BERT-QE: Contextualized Query Expansion for Document Re-ranking (2020, methodology): “2001) and the documents in NPRF (Li et al., 2018); or by the facts that the models used for selecting and re-weighting the expansion information are not powerful enough, as they are essentially scalars based on counting.”
The Simplest Thing That Can Possibly Work: Pseudo-Relevance Feedback Using Text Classification (2019, background): “Also included are results from NPRF, a recently-proposed neural approach to pseudo-relevance feedback [12] (row 19).”
Rethinking Query Expansion for BERT Reranking (2020, background): “There is prior research that shows classic query expansion to be effective for a few neural ranking models where query terms and document terms are matched softly [10] .”
Zero-Shot Contextual Embeddings via Offline Synthetic Corpus Generation (2025, unknown): “Consequently, methods requiring training-time adaptation (e.g., GPL (Wang et al., 2022)) or online corpus access (e.g., pseudo-relevance feedback (Rocchio, 1971; Li et al., 2018)) are considered orthogonal to this specific evaluation.”