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Impact of Noise on Deep Learning-Based Pseudo-Online Gesture Recognition with High-Density EMG
Taleshi M +5 more
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Score distributions for Pseudo Relevance Feedback [PDF]
Information Sciences, 2014 Abstract Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical language modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the Pseudo Relevance Feedback task.
Javier Parapar +2 more
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Pseudo relevance feedback optimization
Information Retrieval Journal, 2021We propose a method for automatic optimization of pseudo relevance feedback (PRF) in information retrieval. Based on the conjecture that the initial query’s contribution to the final query may not be necessary once a good model is built from pseudo relevant documents, we set out to optimize per query only the number of top-retrieved documents to be ...
Avi Arampatzis +2 more
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Collaborative pseudo-relevance feedback
Expert Systems With Applications, 2013Pseudo-relevance feedback (PRF) is a technique commonly used in the field of information retrieval. The performance of PRF is heavily dependent upon parameter values. When relevance judgements are unavailable, these parameters are difficult to set. In the following paper, we introduce a novel approach to PRF inspired by collaborative filtering (CF). We
Dong Zhou, Jianxun Liu
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Pseudo-Relevance Feedback with Dense Retrievers in Pyserini
Proceedings of the 26th Australasian Document Computing Symposium, 2022Transformer-based Dense Retrievers (DRs) are attracting extensive attention because of their effectiveness paired with high efficiency. In this context, few Pseudo-Relevance Feedback (PRF) methods applied to DRs have emerged. However, the absence of a general framework for performing PRF with DRs has made the empirical evaluation, comparison and ...
Hang Li 0009 +4 more
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Exploring term temporality for pseudo-relevance feedback
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 2011As digital collections expand, the importance of the temporal aspect of information has become increasingly apparent. The aim of this paper is to investigate the effect of using long-term temporal profiles of terms in information retrieval by enhancing the term selection process of pseudo-relevance feedback (PRF).
Stewart Whiting +2 more
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Hybrid pseudo-relevance feedback for microblog retrieval
Journal of Information Science, 2013The microblog has become a new global hot spot. Information retrieval (IR) technologies are necessary for accessing the massive amounts of valuable user-generated contents in the microblog sphere. The challenge in searching relevant microblogs is that they are usually very short with sparse vocabulary and may fail to match queries.
Chen, Lin +3 more
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Flexible pseudo-relevance feedback via selective sampling
ACM Transactions on Asian Language Information Processing, 2005Although 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
Sakaitetsuya
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Multimedia Search with Pseudo-relevance Feedback
2003We present an algorithm for video retrieval that fuses the decisions of multiple retrieval agents in both text and image modalities. While the normalization and combination of evidence is novel, this paper emphasizes the successful use of negative pseudo-relevance feedback to improve image retrieval performance.
Rong Yan +2 more
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Pseudo-relevance feedback query based on Wikipedia
2012 IEEE International Conference on Granular Computing, 2012The traditional information retrieval (IR) model always only use the BOW (bag-of-words)-based retrieval model or Concepts-based retrieval model. However BOW-based model ignore the rich semantic relations between the words and text, and Concept-based model always bring in the noisy concepts and loss the precision.
Tingting He, Xionglu Dai
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