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Anserini

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Anserini is an open-source information retrieval toolkit built on Lucene that aims to bridge the gap between academic information retrieval research and the practice of building real-world search applications. This effort grew out of a reproducibility study of various open-source retrieval engines in 2016 (Lin et al., ECIR 2016). See Yang et al. (SIGIR 2017) and Yang et al. (JDIQ 2018) for overviews.

If you've found Anserini to be helpful, we have a simple request for you to contribute back. In the course of replicating baseline results on standard test collections, please let us know if you're successful by sending us a pull request with a simple note, like what appears at the bottom of the Robust04 page. Replicability is important to us, and we'd like to know about successes as well as failures. Since the regression documentation is auto-generated, pull requests should be sent against the raw templates. In turn, you'll be recognized as a contributor.

Getting Started

A zero effort way to try out Anserini is to look at our online colab demo! Click "Open in Playground" and you'll be able to replicate our baselines from the TREC 2004 Robust Track just from the browser!

Main dependencies:

  • Anserini was recently upgraded to Java 11 at commit 17b702d (7/11/2019) from Java 8. Maven 3.3+ is also required.
  • Anserini was upgraded to Lucene 8.0 as of commit 75e36f9 (6/12/2019); prior to that, the toolkit uses Lucene 7.6. Based on preliminary experiments, query evaluation latency has been much improved in Lucene 8. As a result of this upgrade, results of all regressions have changed slightly. To replicate old results from Lucene 7.6, use v0.5.1.

After cloning our repo, build using Maven:

mvn clean package appassembler:assemble

The eval/ directory contains evaluation tools and scripts, including trec_eval, gdeval.pl, ndeval. Before using trec_eval, unpack and compile it, as follows:

tar xvfz trec_eval.9.0.4.tar.gz && cd trec_eval.9.0.4 && make

Before using ndeval, compile it as follows:

cd ndeval && make

Running Standard IR Experiments

Anserini is designed to support experiments on various standard TREC collections out of the box. Each collection is associated with regression tests for replicability. Note that these regressions capture the "out of the box" experience, based on default parameter settings.

Other experiments:

See this page for additional documentation.

Integrations

  • Use Anserini in Python via Pyserini!
  • Anserini integrates with SolrCloud via Solrini!
  • Anserini integrates with Elasticsearch via Elasterini!

Release History

References

Acknowledgments

This research is supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada. Previous support came from the U.S. National Science Foundation under IIS-1423002 and CNS-1405688. Any opinions, findings, and conclusions or recommendations expressed do not necessarily reflect the views of the sponsors.