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). Additional details can be found in a short paper (Yang et al., SIGIR 2017) and a journal article (Yang et al., JDIQ 2018).
Anserini requires Java 8 (note that there are known issues with Java 10 and Java 11) and Maven 3.3+. Oracle JVM is necessary to replicate our regression results; there are known issues with OpenJDK (see this and this). Build using Maven:
mvn clean package appassembler:assemble
tar xvfz trec_eval.9.0.4.tar.gz && cd trec_eval.9.0.4 && make
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.
- Experiments on Disks 1 & 2
- Experiments on Disks 4 & 5 (Robust04)
- Experiments on AQUAINT (Robust05)
- Experiments on the New York Times (Core17)
- Experiments on the Washington Post (Core18)
- 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 Complex Answer Retrieval v1.5 (CAR17)
- Experiments on Complex Answer Retrieval v2.0 (CAR17)
- Experiments on MS MARCO
- Runbooks for TREC 2018: [Anserini group] [h2oloo group]
- Runbook for ECIR 2019 paper on axiomatic semantic term matching
- Runbook for ECIR 2019 paper on cross-collection relevance feedback
IndexUtilsis a utility to interact with an index using the command line (e.g., print index statistics). Refer to
target/appassembler/bin/IndexUtils -hfor more details.
MapCollectionsis a generic mapper framework for processing a document collection in parallel. Developers can write their own mappers for different tasks: one simple example is
CountDocumentMapperwhich 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
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-0.4.1-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.docid # the internal Lucene docid of the 1st hit hits.ldocid # the score of the 1st hit hits.score # the full document of the 1st hit hits.content
- v0.4.0: March 4, 2019 [Release Notes]
- v0.3.0: December 16, 2018 [Release Notes]
- v0.2.0: September 10, 2018 [Release Notes]
- v0.1.0: July 4, 2018 [Release Notes]
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.
Peilin Yang, Hui Fang, and Jimmy Lin. Anserini: Reproducible Ranking Baselines Using Lucene. Journal of Data and Information Quality, 10(4), Article 16, 2018.
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.