Results 241 to 250 of about 142,435 (303)

PGT: Pseudo Relevance Feedback Using a Graph-Based Transformer

European Conference on Information Retrieval, 2021
Most research on pseudo relevance feedback (PRF) has been done in vector space and probabilistic retrieval models. This paper shows that Transformer-based rerankers can also benefit from the extra context that PRF provides. It presents PGT, a graph-based
HongChien Yu, Zhuyun Dai, Jamie Callan
semanticscholar   +1 more source

Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study

European Conference on Information Retrieval, 2021
Pseudo-Relevance Feedback (PRF) utilises the relevance signals from the top-k passages from the first round of retrieval to perform a second round of retrieval aiming to improve search effectiveness.
Hang Li, Shengyao Zhuang, Ahmed Mourad, Xueguang Ma, Jimmy J. Lin, G. Zuccon   +5 more
semanticscholar   +1 more source

Pseudo-Relevance Feedback for Multimedia Retrieval

2003
Video information retrieval requires a system to find information rele-vant to a query which may be presented simultaneously in different ways through a text description, audio, still images and/or video sequences. The actual search also takes place in the text, audio, image or video domain.
Rong Yan, Alexander G. Hauptmann, Rong Jin   +2 more
openaire   +1 more source

Flexible pseudo-relevance feedback via selective sampling

ACM Transactions on Asian Language Information Processing, 2005
Although Pseudo-Relevance Feedback (PRF) is a widely used technique for enhancing average retrieval performance, it may actually hurt performance for around one-third of a given set of topics. To enhance the reliability of PRF, Flexible PRF has been proposed, which adjusts the number of pseudo-relevant documents and/or the number of expansion terms for
Tetsuya Sakai, Toshihiko Manabe, Makoto Koyama   +2 more
openaire   +1 more source

A Multi-Dimensional Semantic Pseudo-Relevance Feedback Information Retrieval Model

2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2022
Recently neural information retrieval systems have spurred many successful applications. Retrieval model to obtain a candidate document collection in the first retrieval stage, then use BERT to sort the candidate documents.
Min Pan, Yu Liu, Quanli Pei, Huixian Mao, Aoqun Jin, Sheng Huang, Yinhan Yang   +6 more
semanticscholar   +1 more source

TPRF: A Transformer-based Pseudo-Relevance Feedback Model for Efficient and Effective Retrieval

arXiv.org
This paper considers Pseudo-Relevance Feedback (PRF) methods for dense retrievers in a resource constrained environment such as that of cheap cloud instances or embedded systems (e.g., smartphones and smartwatches), where memory and CPU are limited and ...
Chu-Chun Yu, Hang Li, Ahmed Mourad, B. Koopman, G. Zuccon   +4 more
semanticscholar   +1 more source

Pseudo-Relevance Feedback Based on Matrix Factorization

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016
In information retrieval, pseudo-relevance feedback (PRF) refers to a strategy for updating the query model using the top retrieved documents. PRF has been proven to be highly effective in improving the retrieval performance. In this paper, we look at the PRF task as a recommendation problem: the goal is to recommend a number of terms for a given query
Hamed Zamani, Javid Dadashkarimi, Azadeh Shakery, W. Bruce Croft   +3 more
openaire   +1 more source

Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations

arXiv.org
Information-seeking dialogue systems are widely used in e-commerce systems, with answers that must be tailored to fit the specific settings of the online system.
Xiaoqing Zhang, Xiuying Chen, Shen Gao, Shuqi Li, Xin Gao, Ji-Rong Wen, Rui Yan   +6 more
semanticscholar   +1 more source

Block-based pseudo-relevance feedback for image retrieval

Journal of Experimental & Theoretical Artificial Intelligence, 2021
Pseudo-relevance feedback (PRF) is a relevance feedback (RF) technique for information retrieval that treats the top k retrieved images as relevance feedback.
openaire   +1 more source

Improving pseudo-relevance feedback via tweet selection

Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013
Query expansion methods using pseudo-relevance feedback have been shown effective for microblog search because they can solve vocabulary mismatch problems often seen in searching short documents such as Twitter messages (tweets), which are limited to 140 characters.
Taiki Miyanishi, Kazuhiro Seki, Kuniaki Uehara   +2 more
openaire   +1 more source