Search Results for author: Joel Mackenzie

Found 9 papers, 6 papers with code

How Much Freedom Does An Effectiveness Metric Really Have?

1 code implementation18 Sep 2023 Alistair Moffat, Joel Mackenzie

It is tempting to assume that because effectiveness metrics have free choice to assign scores to search engine result pages (SERPs) there must thus be a similar degree of freedom as to the relative order that SERP pairs can be put into.

Exploring the Representation Power of SPLADE Models

1 code implementation29 Jun 2023 Joel Mackenzie, Shengyao Zhuang, Guido Zuccon

The SPLADE (SParse Lexical AnD Expansion) model is a highly effective approach to learned sparse retrieval, where documents are represented by term impact scores derived from large language models.


Efficient Immediate-Access Dynamic Indexing

no code implementations11 Nov 2022 Alistair Moffat, Joel Mackenzie

In a dynamic retrieval system, documents must be ingested as they arrive, and be immediately findable by queries.


Faster Learned Sparse Retrieval with Guided Traversal

1 code implementation24 Apr 2022 Antonio Mallia, Joel Mackenzie, Torsten Suel, Nicola Tonellotto

Neural information retrieval architectures based on transformers such as BERT are able to significantly improve system effectiveness over traditional sparse models such as BM25.

Information Retrieval Retrieval

A Sensitivity Analysis of the MSMARCO Passage Collection

no code implementations6 Dec 2021 Joel Mackenzie, Matthias Petri, Alistair Moffat

The recent MSMARCO passage retrieval collection has allowed researchers to develop highly tuned retrieval systems.

Passage Retrieval Retrieval

Wacky Weights in Learned Sparse Representations and the Revenge of Score-at-a-Time Query Evaluation

no code implementations22 Oct 2021 Joel Mackenzie, Andrew Trotman, Jimmy Lin

Recent advances in retrieval models based on learned sparse representations generated by transformers have led us to, once again, consider score-at-a-time query evaluation techniques for the top-k retrieval problem.


Anytime Ranking on Document-Ordered Indexes

1 code implementation18 Apr 2021 Joel Mackenzie, Matthias Petri, Alistair Moffat

Inverted indexes continue to be a mainstay of text search engines, allowing efficient querying of large document collections.

Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format

2 code implementations18 Mar 2020 Jimmy Lin, Joel Mackenzie, Chris Kamphuis, Craig Macdonald, Antonio Mallia, Michał Siedlaczek, Andrew Trotman, Arjen de Vries

There exists a natural tension between encouraging a diverse ecosystem of open-source search engines and supporting fair, replicable comparisons across those systems.

Boosting Search Performance Using Query Variations

1 code implementation15 Nov 2018 Rodger Benham, Joel Mackenzie, Alistair Moffat, J. Shane Culpepper

Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set.

Re-Ranking Retrieval

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