Anserini

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) and the initial vision of our system is described in a short paper (Yang et al., SIGIR 2017).

Getting Started

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.tar.gz && cd trec_eval.9.0 && 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:

• Experiments on Disks 1 & 2
• Experiments on Disks 4 & 5 (Robust04)
• Experiments on AQUAINT (Robust05)
• Experiments on New York Times (Core17)
• Experiments on Wt10g
• Experiments on Gov2
• Experiments on ClueWeb09 (Category B)
• Experiments on ClueWeb12-B13
• Experiments on ClueWeb12
• Experiments on Tweets2011 (MB11 & MB12)
• Experiments on Tweets2013 (MB13 & MB14)
• Experiments on CAR17

Additional Documentation

• Axiomatic Reranking

• IndexUtils is a utility to interact with an index using the command line (e.g., print index statistics). Refer to target/appassembler/bin/IndexUtils -h for more details.

• MapCollections is a generic mapper framework for processing a document collection in parallel. Developers can write their own mappers for different tasks: one simple example is CountDocumentMapper which counts the number of documents in a collection:

  target/appassembler/bin/MapCollections -collection ClueWeb09Collection \
    -threads 16 -input ~/collections/web/ClueWeb09b/ClueWeb09_English_1/ \
    -mapper CountDocumentMapper -context CountDocumentMapperContext
  

Python Interface

Anserini was designed with Python integration in mind, for connecting with popular deep learning toolkits such as PyTorch. This is accomplished via pyjnius. The SimpleSearcher class provides a simple Python/Java bridge, shown below:

import jnius_config
jnius_config.set_classpath("target/anserini-0.1.1-SNAPSHOT-fatjar.jar")

from jnius import autoclass
JString = autoclass('java.lang.String')
JSearcher = autoclass('io.anserini.search.SimpleSearcher')

searcher = JSearcher(JString('lucene-index.robust04.pos+docvectors+rawdocs'))
hits = searcher.search(JString('hubble space telescope'))

# the docid of the 1st hit
hits[0].docid

# the internal Lucene docid of the 1st hit
hits[0].ldocid

# the score of the 1st hit
hits[0].score

# the full document of the 1st hit
hits[0].content

Release History

• v0.1.0: July 4, 2018 [Release Notes]
• v0.2.0: September 10, 2018 [Release Notes]

References

• Jimmy Lin, Matt Crane, Andrew Trotman, Jamie Callan, Ishan Chattopadhyaya, John Foley, Grant Ingersoll, Craig Macdonald, Sebastiano Vigna. Toward Reproducible Baselines: The Open-Source IR Reproducibility Challenge. Proceedings of the 38th European Conference on Information Retrieval (ECIR 2016), pages 408-420, March 2016, Padua, Italy.

• Peilin Yang, Hui Fang, and Jimmy Lin. Anserini: Enabling the Use of Lucene for Information Retrieval Research. Proceedings of the 40th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017), pages 1253-1256, August 2017, Tokyo, Japan.

Acknowledgments

This research has been supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada and 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.