# ielab / clf

Experimental Settings & Results for ECIR 2020 Paper: You Can Teach an Old Dog New Tricks: Rank Fusion applied to Coordination Level Matching for Ranking in Systematic Reviews
Shell Python

## Latest commit

Latest commit 33c335b Dec 24, 2019

## Files

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evaluation Dec 24, 2019
ranking Dec 24, 2019
results Dec 24, 2019

# Coordination Level Fusion

This repository contains the experimental pipeline scripts to produce the results in the paper "You Can Teach an Old Dog New Tricks: Rank Fusion applied to Coordination Level Matching for Ranking in Systematic Reviews", presented at ECIR 2020. You may cite this research as:

@inproceedings{scells2020clf,
Author = {Scells, Harrisen and Zuccon, Guido and Koopman, Bevan},
Booktitle = {Proceedings of the 42nd European Conference on Information Retrieval},
Title = {You \textit{can} Teach an Old Dog New Tricks: Rank Fusion applied to Coordination Level Matching for Ranking in Systematic Reviews},
Year = {2020}
}


There are a couple of tools that need to be installed that will allow one to replicate the experiments. These tools are as follows:

This repository is split into two parts: ranking and evaluation.

## Ranking

The scripts to run the ranking experiments can be found in the ranking folder. The clf.sh file must be edited to point to the boogie main file.

### CLF Experiments:

The following two scripts produce rankings for the TAR'17 and TAR'18 queries. The files and folders that are referenced in the scripts are available in the ranking folder.

#### TAR'17

./clf.sh false true true true false false false tar17_testing_pmids tar17_testing_titles 1 tar17_testing_topics tar17_clf_testing.run

#### TAR'18

./clf.sh false true true true false false false tar2_testing_pmids tar2_testing_titles 1 tar2_testing_topics tar18_clf_testing.run

Note that in order to change the weighting schemes, the source code must be directly modified. The place to do so is located here. The arguments to the script are available at the top of the file. There are also three arguments that must be replaced in the clf.json file. These can be obtained by signing up for an API key with ncbi (https://www.ncbi.nlm.nih.gov/).

### Cut-Off Experiments:

The following two scripts produce cut-off rankings for the TAR'17 and TAR'18 queries. The scripts are pre-configured to sweep the parameters listed in the paper. The files and folders that are referenced in the scripts are available in the ranking folder.

#### TAR'17

./clf_rm.sh tar17_training_pmids tar17_training_titles clf_rm_gain_2017_ tar17_training_topics

#### TAR'18

./clf_rm.sh tar2_training_pmids tar2_training_titles clf_rm_gain_2018_ tar2_training_topics

## Evaluation

The following two scripts evaluate the main ranking task of TAR and the cut-off tasks. Both scripts evaluate the TAR'17 & '18 scripts at the same time. Note that all of the final evaluation and run files used in the paper are already provided, including the cut-off runs. The run files (from these experiments, and the same run files used from the TAR collections) can be found in evaluation/ranking_runs and evaluation/cutoff_runs respectively. The runs for the grid search can be found in evaluation/cutoff_gs. The results from running the below scripts can be found in the results folder. Each subdirectory of results is categorised into evaluation results for ranking, cut-offs and the grid search for the cut-off parameter. Within each of these subdirectories, the results are further split into the respective TAR collection. Finally, the results are available in one of two folders: the split folder contains the evaluation results from the three different tools needed to cover all the evaluation measures, and the combined folder contains json files with these measures combined into a single JSON object. For example, the results/ranking/TAR17/combined/clf+weighting+pubmed+qe_2017.run.eval.json folder contains the combined evaluation output of the best performing CLF weighting scheme on the TAR'17 collection.

The scripts provided below are only for the purposes of reproducibility and awareness.

### Ranking Evaluation:

./evaluate_ranking.sh

### Cut-Off Evaluation:

./evaluate_cutoff.sh