A cascade ranking model for efficient ranked retrieval

By Lidan Wang, Jimmy J. Lin, Donald Metzler. 2011

280 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Information Retrieval & Search

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Citations in the corpus

Re2G: Retrieve, Rerank, Generate (2022, background): “It has been previously established that results from initial retrieval can be greatly improved through the use of a reranker [Liu, 2009, Wang et al., 2011].”
Multi-Stage Document Ranking with BERT (2019, methodology): “This basic approach has received much interest in academia (Matveeva et al., 2006; Wang et al., 2011; Asadi and Lin, 2013; Chen et al., 2017; Mackenzie et al., 2018) as well as industry.”
Anserini (2018, background): “What about reproducibility beyond simple bag-of-words ranking models? Although we have not explicitly addressed this (next) challenge, we believe that baseline bag-of-words ranking models also have an important role to play in modern multi-stage ranking architectures, which have been explored by res”
Anserini: Enabling the Use of Lucene for Information Retrieval Research (2017, background): “In particular, we advocate a multi-stage ranking architecture [3, 6, 14, 17] so that researchers will not need to directly work with native Lucene scoring APIs.”
Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations (2021, background): “In particular, the importance of stable first-stage retrieval within a multi-stage ranking architecture [5, 25, 34] has become even more important, as it provides the foundation for increasingly-complex modern approaches that leverage hybrid techniques.”
Efficient Document Re-Ranking for Transformers by Precomputing Term Representations (2020, background): “These approaches typically act as a final-stage ranking function, via a telescoping (also referred to as cascading, or multi-stage) technique [29, 43]; that is, initial ranking is conducted with less expensive approaches (e.”
Reranking for Efficient Transformer-based Answer Selection (2020, methodology): “, the use of sequential rerankers [19].”
Query Driven Algorithm Selection in Early Stage Retrieval (2017, background): “For example, research groups can focus on early stage retrieval efficiency [4, 16, 54], balancing feature costs [12, 53, 56], or improving the performance of the learning-to-rank algorithms [5, 26, 34, 35, 37].”
Fine-Tuning LLaMA for Multi-Stage Text Retrieval (2023, background): “While multi-stage retrieval pipelines date back well over a decade (Matveeva et al., 2006; Cambazoglu et al., 2010; Wang et al., 2011), they have bene-”
Efficient Cost-Aware Cascade Ranking in Multi-Stage Retrieval (2017, methodology): “Our implementations of these ranking models are based on Baselines algorithm described in Wang et al. [36], using the suggested se � ing � = 0 .”
Efficiency Implications of Term Weighting for Passage Retrieval (2020, background): “Hence, these systems are often implemented as multi-stage cascades, where a set of increasingly sophisticated ranking models re-rank a decreasing number of documents, arriving at a final ranked results list [24].”
Training Efficient Tree-Based Models for Document Ranking (2013, background): “[5] explore a cascade of linear models, whereas we focus on tree-based models.”
Overview of the TREC 2020 Deep Learning Track (2021, background): “This is a common scenario in many real-world retrieval systems that employ a telescoping architecture [Matveeva et al., 2006, Wang et al., 2011].”
A Comparison of Document-at-a-Time and Score-at-a-Time Query Evaluation (2017, background): “For multi-stage ranking algorithms [4, 36] that may require large values of k, JASS may be a good choice.”
The Cascade Transformer: an Application for Efficient Answer Sentence Selection (2020, background): “For example, Wang et al. (2011) formulated and developed a cascade ranking model that improved both top-k ranked effectiveness and retrieval efﬁciency.”
Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset (2020, methodology): “In our design, initial retrieval is performed by the Anserini IR toolkit (Yang et al., 2017, 2018),1 which we have been developing for several years and powers a number of our previous systems that incorporate various neural architectures (Yang et al., 2019; Yilmaz et al., 2019).”