WIKIR

A python tool for building large scale Wikipedia-based Information Retrieval datasets

Currently supported languages:
English
French
Spanish
Italian

Requirements
Installation
Usage
Details
Example
Reproducibility
Downloads
More languages
Citation
References

Requirements

Python 3.6+
NumPy and SciPy
pytrec_eval to evaluate the runs
nltk library to perform stemming and stopword removal in several languages
Pandas library to be able to save the dataset as a dataframe compatible with MatchZoo
Optional:
- Rank-BM25 as a first efficient ranking stage if you want to use MatchZoo
- MatchZoo in order to train and evaluate neural networks on the collection

Installation

Install wikIR

git clone --recurse-submodules https://github.com/getalp/wikIR.git
cd wikIR
pip install -r requirements.txt

Install Rank-BM25 (optional)

pip install git+ssh://git@github.com/dorianbrown/rank_bm25.git

Install MatchZoo (optional)

git clone https://github.com/NTMC-Community/MatchZoo.git
cd MatchZoo
python setup.py install

Usage

Download and extract a XML wikipedia dump file from here
Use Wikiextractor to get the text of the wikipedia pages in a signle json file, for example :

python wikiextractor/WikiExtractor.py input --output - --bytes 100G --links --quiet --json > output.json

Where input is the XML wikipedia dump file and output is the output in json format

Call our script

python build_wikIR.py [-i,--input] [-o,--output_dir] 
                      [-m,--max_docs] [-d,--len_doc] [-q,--len_query] [-l,--min_len_doc]
                      [-e,--min_nb_rel_doc] [-v,--validation_part] [-t,--test_part]
                      [-k,--k] [-i,--title_queries] [-f,--only_first_links] 
                      [-s,--skip_first_sentence] [-c,--lower_cased] [-j,--json] 
                      [-x,--xml] [-b,--bm25] [-r,--random_seed]

arguments : 

    [-i,--input]                  The json file produced by wikiextractor
    
    [-o,--output_dir]             Directory where the collection will be stored

optional argument:

    [--language]                  Language of the input json file
                                  Possible values: 'en','fr','es','it'
                                  Default value: 'en'

    [-m,--max_docs]               Maximum number of documents in the collection
                                  Default value None

    [-d,--len_doc]                Number of max tokens in documents
                                  Default value None: all tokens are preserved
                                  
    [-q,--len_query]              Number of max tokens in queries
                                  Default value None: all tokens are preserved
    
    [-l,--min_len_doc]            Mininum number of tokens required for an article 
                                  to be added to the dataset as a document
                                  Default value 200
    
    [-e,--min_nb_rel_doc]         Mininum number of relevant documents required for 
                                  a query to be added to the dataset
                                  Default value 5
    
    [-v,--validation_part]        Number of queries in the validation set
                                  Default value 1000
    
    [-t,--test_part]              Number of queries in the test set
                                  Default value 1000
    
    [-i,--title_queries]          If used, queries are build using the title of 
                                  the article 
                                  If not used, queries are build using the first
                                  sentence of the article
    
    [-f,--only_first_links]       If used, only the links in the first sentence of 
                                  articles will be used to build qrels
                                  If not used, all links up to len_doc token will
                                  be used to build qrels
    
    [-s,--skip_first_sentence]    If used, the first sentence of articles is not 
                                  used in documents
    
    [-c,--lower_cased]            If used, all characters are lowercase
    
    [-j,--json]                   If used, documents and queries are saved in json
                                  If not used, documents and queries are saved in
                                  csv as dataframes compatible with matchzoo
                                  
    [-x,--xml]                    If used, documents and queries are saved in xml
                                  format compatible with Terrier IRS 
                                  If not used, documents and queries are saved in
                                  csv as dataframes compatible with matchzoo                                  
                                  
    [-b,--bm25]                   If used, perform and save results of BM25 ranking 
                                  model on the collection
                                
    [-k,--k]                      If BM25 is used, indicates the number of documents 
                                  per query saved 
                                  Default value 100
    
    [-r,--random_seed]            Random seed
                                  Default value 27355

Details

The data construction process is similar to [1] and [2]
Article used to build the documents (article titles are removed from documents)
Title or first sentence of each article is used to build the queries
We assign a relevance of 2 if the query and document were extracted from the same article
We assign a relevance of 1 if there is a link from the article of the document to the article of the query
- For example the document Autism is relevant to the query Developmental disorder.

Example

Execute the follwing lines in the wikIR directory

Download the english wikipedia dump from 01/11/2019

wget https://dumps.wikimedia.org/enwiki/20191101/enwiki-20191101-pages-articles-multistream.xml.bz2

Extract the file

bzip2 -dk enwiki-20191101-pages-articles-multistream.xml.bz2

Use Wikiextractor (ignore the WARNING: Template errors in article)

python wikiextractor/WikiExtractor.py enwiki-20191101-pages-articles-multistream.xml --output - --bytes 100G --links --quiet --json > enwiki.json

Use wikIR builder

python build_wikIR.py --input enwiki.json --output_dir wikIR1k --max_docs 370000 -tfscb

⚠️ Do not forget to delete the dowloaded and intermediary files ⚠️

rm enwiki-20191101-pages-articles-multistream.xml.bz2
rm enwiki-20191101-pages-articles-multistream.xml
rm wiki.json

Train and evaluate neural networks with Matchzoo

python wikIR/matchzoo_experiment.py -c config.json

Display results in a format compatible with a latex table

python wikIR/display_res.py -c config.json

Downloads

The wikIR1k and wikIR59k datasets presented in our paper are available for download

You can download wikIR1k here.

You can download wikIR59k here.

More languages

Datasets in more languages are also available:

	English	French	Spanish	Italian
title queries	ENwikIR59k	FRwikIR14k	ESwikIR13k	ITwikIR16k
first sentence queries	ENwikIRS59k	FRwikIRS14k	ESwikIRS13k	ITwikIRS16k

We propose datasets with short and well defined queries built from titles (ENwikIR59k, FRwikIR14k) and datasets with long and noisy queries built from first sentences (ENwikIRS59k, FRwikIRS14k) to study robustness of IR models to noise.

Reproducibility

Reproduce wikIR1k dataset

To reproduce the wikIR1k dataset, execute the follwing lines in the wikIR directory

wget https://dumps.wikimedia.org/enwiki/20191101/enwiki-20191101-pages-articles-multistream.xml.bz2
bzip2 -d enwiki-20191101-pages-articles-multistream.xml.bz2
python wikiextractor/WikiExtractor.py enwiki-20191101-pages-articles-multistream.xml --output - --bytes 100G --links --quiet --json > enwiki.json
rm enwiki-20191101-pages-articles-multistream.xml.bz2
rm enwiki-20191101-pages-articles-multistream.xml
python build_wikIR.py --input enwiki.json --output_dir COLLECTION_PATH/wikIR1k --max_docs 370000 --validation_part 100 --test_part 100 -tfscb
rm enwiki.json

COLLECTION_PATH is the directory where wikIR1k will be stored

Reproduce wikIR59k dataset

To reproduce the wikIR59k dataset, execute the follwing lines in the wikIR directory

wget https://dumps.wikimedia.org/enwiki/20191101/enwiki-20191101-pages-articles-multistream.xml.bz2
bzip2 -d enwiki-20191101-pages-articles-multistream.xml.bz2
python wikiextractor/WikiExtractor.py enwiki-20191101-pages-articles-multistream.xml --output - --bytes 100G --links --quiet --json > enwiki.json
rm enwiki-20191101-pages-articles-multistream.xml.bz2
rm enwiki-20191101-pages-articles-multistream.xml
python build_wikIR.py --input enwiki.json --output_dir COLLECTION_PATH/wikIR59k --validation_part 1000 --test_part 1000 -tfscb
rm enwiki.json

COLLECTION_PATH is the directory where wikIR59k will be stored

⚠️ bm25 can take several days to solve all the queires on wikIR59k, therefore the bm25 results files are provided in the dowloadable datasets.

Reproduce both datasets

To create both wikIR1k and wikIR59k datasets just call the following script

./reproduce_datasets.sh COLLECTION_PATH

COLLECTION_PATH is the directory where the datasets will be stored

Train and evaluate neural networks for ad-hoc IR with matchzoo

To reproduce our results with matchzoo models on the dev dataset, call

python matchzoo_experiment.py -c config.json

⚠️ bm25 results files are needed by matchzoo_experiment.py

Display results

To compute statistical significance against BM25 with Student t-test with Bonferroni correction and display the results of the dev dataset, call

python display_res.py -c config.json

⚠️ Change "collection_path" in the config.json file if you want to train and display results on the full dataset

Citation

If you use wikIR tool or the dataset(s) we provide to produce results for your scientific publication, please refer to our paper:

@inproceedings{frej-etal-2020-wikir,
title = "{WIKIR}: A Python Toolkit for Building a Large-scale {W}ikipedia-based {E}nglish Information Retrieval Dataset",
author = "Frej, Jibril and
Schwab, Didier and
Chevallet, Jean-Pierre",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.237",
pages = "1926--1933",
abstract = "Over the past years, deep learning methods allowed for new state-of-the-art results in ad-hoc information retrieval. However such methods usually require large amounts of annotated data to be effective. Since most standard ad-hoc information retrieval datasets publicly available for academic research (e.g. Robust04, ClueWeb09) have at most 250 annotated queries, the recent deep learning models for information retrieval perform poorly on these datasets. These models (e.g. DUET, Conv-KNRM) are trained and evaluated on data collected from commercial search engines not publicly available for academic research which is a problem for reproducibility and the advancement of research. In this paper, we propose WIKIR: an open-source toolkit to automatically build large-scale English information retrieval datasets based on Wikipedia. WIKIR is publicly available on GitHub. We also provide wikIR59k: a large-scale publicly available dataset that contains 59,252 queries and 2,617,003 (query, relevant documents) pairs.",
language = "English",
ISBN = "979-10-95546-34-4",
}

References

[1] Shota Sasaki, Shuo Sun, Shigehiko Schamoni, Kevin Duh, and Kentaro Inui. 2018. Cross-lingual learning-to-rank with shared representations, pdf

[2] Shigehiko Schamoni, Felix Hieber, Artem Sokolov, and Stefan Riezler. 2014. Learning translational and knowledge-based similarities from relevance rankings for cross-language retrieval, pdf

getalp/wikIR