Efficient and effective retrieval using selective pruning

By Nicola Tonellotto, Craig Macdonald, Iadh Ounis. 2013

91 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jimmy J. Lin
2. Hui Fang
3. J. Shane Culpepper
4. Roi Blanco
5. C. Clarke
6. Sebastian Bruch
7. Peilin Yang
8. Matt Crane

In Information Systems

1. Jimmy J. Lin
2. Hui Fang
3. Peilin Yang
4. Rodrigo Nogueira
5. J. Shane Culpepper
6. Zeynep Akkalyoncu Yilmaz
7. Joel M. Mackenzie
8. C. Clarke

In Signal Processing

1. Matt Crane
2. Joel M. Mackenzie
3. Peilin Yang
4. J. Shane Culpepper
5. Jimmy J. Lin
6. Hui Fang
7. Roi Blanco
8. C. Clarke

Citations in the corpus

• Anserini (2018, background): “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 researchers [8, 16, 17, 36, 49, 55] and deployed commercially [33, 43].”
• Query Driven Algorithm Selection in Early Stage Retrieval (2017, methodology): “Since re-ordering can be computationally expensive and is sensitive to the number of documents that must be reordered, minimizing the size of the candidate set is an important problem [9, 12, 36, 48].”
• An Exploration of Approaches to Integrating Neural Reranking Models in Multi-Stage Ranking Architectures (2017, background): “Fundamentally, neural network models (and even more broadly, learning-to-rank approaches) for a variety of information retrieval tasks behave as rerankers in a multi-stage ranking architecture [1, 5, 8, 11, 14, 18, 22].”
• Effectiveness/efficiency tradeoffs for candidate generation in multi-stage retrieval architectures (2013, methodology): “Given a fixed set of features and a learning-to-rank model, we explore effectiveness/efficiency tradeoffs in the candidate generation stage, comparing postings intersection with SvS, conjunctive query evaluation with Wand, and disjunctive query evaluation with Wand.”
• Quality versus efficiency in document scoring with learning-to-rank models (2016, unknown): “The second phase of the scoring process is the fastest part of the ranking process ( Tonellotto et al., 2013 ).”