osirrc / solrini-docker

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OSIRRC Docker Image for Solrini

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Ryan Clancy and Jimmy Lin

This is the docker image for the Anserini toolkit (v0.5.1), with Solr indexing, conforming to the OSIRRC jig for the Open-Source IR Replicability Challenge (OSIRRC) at SIGIR 2019. This image is available on Docker Hub. The OSIRRC 2019 image library contains a log of successful executions of this image.

  • Supported test collections: robust04, core17, core18 (newswire); gov2, cw09b, cw12b (web)
  • Supported hooks: init, index, interact

The search results are the same as Anserini, thus we use those results.

Quick Start

The following jig command can be used to index TREC disks 4/5 for robust04:

python run.py prepare \
  --repo osirrc2019/solrini \
  --tag <tag> \
  --collections robust04=/path/to/disk45=trectext \
  --opts solr_memory=8G solr_version 8.0.0

The following jig command can be used to perform a retrieval run on the collection with the robust04 test collection.

python run.py interact \
  --repo osirrc2019/solrini \
  --tag <tag>

Where <tag> is a valid tag.

Use docker ps to see the container running with a port mapping that looks like 0.0.0.0:[port]->8983/tcp. Navigate to 0.0.0.0:[port] in your web browser to view Solr.

Retrieval Methods

The Anserini image supports the following retrieval methods:

  • BM25: k1=0.9, b=0.4 (Robertson et al., 1995)
  • QL (query likelihood with Dirichlet smooth): mu=1000 (Zhai and Lafferty, 2001)
  • +RM3 (RM3 variant of relevance models, applied on top of initial BM25 or QL results): exact parameter settings (Abdul-Jaleel et al., 2004)
  • +Ax (Semantic term matching in the axiomatic framework, applied on top of initial BM25 or QL results): exact parameter settings (Fang and Zhai, 2006)

Expected Results

The following numbers should be able to be re-produced using the scripts provided in the bin directory.

robust04

MAP BM25 +RM3 +Ax QL +RM3 +Ax
TREC 2004 Robust Track Topics 0.2531 0.2903 0.2895 0.2467 0.2747 0.2774

core17

MAP BM25 +RM3 +Ax QL +RM3 +Ax
TREC 2017 Common Core Track Topics 0.2087 0.2823 0.2787 0.2032 0.2606 0.2613

core18

MAP BM25 +RM3 +Ax QL +RM3 +Ax
TREC 2018 Common Core Track Topics 0.2495 0.3136 0.2920 0.2526 0.3073 0.2966

gov2

MAP BM25 +RM3 +Ax QL +RM3 +Ax
TREC 2004 Terabyte Track: Topics 701-750 0.2689 0.2844 0.2665 0.2681 0.2708 0.2666
TREC 2005 Terabyte Track: Topics 751-800 0.3390 0.3820 0.3664 0.3303 0.3559 0.3646
TREC 2006 Terabyte Track: Topics 801-850 0.3080 0.3377 0.3069 0.2996 0.3154 0.3084

cw09b

MAP BM25 +RM3 +Ax QL +RM3 +Ax
TREC 2010 Web Track: Topics 51-100 0.1126 0.0933 0.0928 0.1060 0.1019 0.1086
TREC 2011 Web Track: Topics 101-150 0.1094 0.1081 0.0974 0.0958 0.0837 0.0879
TREC 2012 Web Track: Topics 151-200 0.1106 0.1107 0.1315 0.1069 0.1059 0.1212

cw12b

MAP BM25 +RM3 +Ax QL +RM3 +Ax
TREC 2013 Web Track: Topics 201-250 0.0468 0.0412 0.0435 0.0397 0.0322 0.0359
TREC 2014 Web Track: Topics 251-300 0.0224 0.0210 0.0180 0.0235 0.0203 0.0186

Implementation

The following is a quick breakdown of what happens in each of the scripts in this repo.

Dockerfile

The Dockerfile installs dependencies, copies scripts to the root dir, exposes port 8983, and sets the working dir to /work.

init

The init script clones Anserini, installs Solr, and bootstraps Solr with Anserini configs.

index

The index script indexes the collection with Solr.

References

  • Stephen E. Robertson, Steve Walker, Micheline Hancock-Beaulieu, Mike Gatford, and A. Payne. (1995) Okapi at TREC-4. TREC
  • ChengXiang Zhai and John Lafferty. (2001) A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval. SIGIR.
  • Nasreen Abdul-Jaleel, James Allan, W. Bruce Croft, Fernando Diaz, Leah Larkey, Xiaoyan Li, Donald Metzler, Mark D. Smucker, Trevor Strohman, Howard Turtle, and Courtney Wade. (2004) UMass at TREC 2004: Novelty and HARD. TREC.
  • Hui Fang and ChengXiang Zhai. (2006) Semantic Term Matching in Axiomatic Approaches to Information Retrieval. SIGIR.

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