Cross-Language Dataset

Description

This dataset is a multilingual, multi-style and multi-granularity dataset for cross-language textual similarity detection. More precisely, the characteristics of this dataset are the following:

• it is multilingual: French, English and Spanish;
• it proposes cross-language alignment information at different granularities: document-level, sentence-level and chunk-level;
• it is based on both parallel and comparable corpora;
• it contains both human and machine translated text;
• part of it has been altered (to make the cross-language similarity detection more complicated) while the rest remains without noise;
• documents were written by multiple types of authors: from average to professionals.

Scientific paper

A description of the dataset and its building are described in the following paper:

A Multilingual, Multi-Style and Multi-Granularity Dataset for Cross-Language Textual Similarity Detection. Jérémy Ferrero, Frédéric Agnès, Laurent Besacier and Didier Schwab. In the 10th edition of the Language Resources and Evaluation Conference (LREC 2016).

Characteristics

Sub-corpus	Alignment	Authors	Translations	Translators	Alteration	NE (%)
JRC Acquis2	Parallel	Politicians	Human	Professional	No	3.74
Europarl1	Parallel	Politicians	Human	Professional	No	7.74
Wikipedia2	Comparable	Anyone	-	-	Noise	8.37
PAN-PC-113	Parallel	Professional authors	Human	Professional	Yes	3.24
APR (Amazon Product Reviews4)	Parallel	Anyone	Machine	Google Translate	No	6.04
Conference papers	Comparable	Computer scientists	Human	Computer scientists	Noise	9.36

Statistics

Sub-corpus	# Aligned documents	# Aligned sentences	# Aligned noun chunks
JRC-Acquis2	10,000	149,506	10,094
Europarl1	9,431	475,834	25,603
Wikipedia2	10,000	4,792	132
PAN-PC-113	2,920	88,977	1,360
APR (Amazon Product Reviews4)	6,000	23,235	2,603
Conference papers	35	1,304	272

For more statistics, see the stats/ directory.

Repository description

• In the dataset/documents/ directory, you can find the dataset of parallel and comparable files aligned at document-level (one file represents one document).
• In the dataset/sentences/ directory, you can find the dataset of parallel and comparable files aligned at sentence-level (one line of a file represents one sentence).
• In the dataset/chunks/ directory, you can find the dataset of parallel and comparable files aligned at chunk-level (one line of a file represents one noun chunk).
• In the dataset/documents/Conference_papers/ directory, you can also find a pdf_conference_papers/ directory containing the original scientific papers in PDF format.
• In the dataset/*/PAN11/ sub-directories, you can also find a metadata/ directory containing additional information about the PAN-PC-11 alignments.
• In the docs/ directory, you can find the papers related to the dataset.
• In the masks/ directory, you can find the masks (that describe the pairs) used for build the folds during our evaluation.
• In the scripts/ directory, you can find all the useful files to re-build the dataset from the pre-existing corpora.
• In the stats/ directory, you can find the XLSX file with statistics on the dataset.
• In the study/ directory, you can find XLSX files related to the study conducted in the BUCC 2017 paper: Deep Investigation of Cross-Language Plagiarism Detection Methods.

Scripts directory

This directory contains scripts that we used for corpus building. We also provide them in case somebody would be interested to extend the corpus.

• In the scripts/chunking/ directory, you can find a script to extract noun chunks from a POS sequence from TreeTagger⁵.
  
• In the scripts/create_translations_dico/ directory, you can find a script to build an unigram translation dictionary for the use of HunAlign⁶.
  
• In the scripts/create_verif_align/ directory, you can find a script to print and save the alignments in a readable format.
  
• In the scripts/enrich_dico_with_dbnary/ directory, you can find a script to enrich an unigram translation dictionary with DBNary⁷ entries.
  
• In the scripts/parse_APR_collection/ directory, you can find a script to parse the Webis-CLS-10⁴ corpus and extract the English-French pairs.
  
• In the scripts/parse_PAN_collection/ directory, you can find a script to parse the PAN-PC-11³ corpus and extract the English-Spanish pairs with metadata.
  
• In the scripts/parse_conf_papers_bibtex/ directory, you can find a script to parse the TALN BibTeX⁸, crawl the web and thus allow the construction of French-English conference paper pairs.
  

To manage the encoding of the files, we use the ForceUTF8⁹ class coded by Sebastián Grignoli.
To detect the language of a text, we use the PHP implementation¹⁰ by Nicholas Pisarro of the Cavnar and Trenkle (1994)¹¹ classification algorithm.
To query DBNary⁷, we use PHP class-interfaces¹².

If you have additional questions, please send it to me by email at [email protected].

References, tools used and pre-existing collections

1. Europarl
   Philipp Koehn (2005).
   Europarl: A Parallel Corpus for Statistical Machine Translation.
   In Conference Proceedings: the tenth Machine Translation Summit, pages 79–86. AAMT.
   url: http://opus.lingfil.uu.se/Europarl.php

2. CL-PL-09 (JRC-Acquis + Wikipedia)
   Martin Potthast, Alberto Barrón-Cedeño, Benno Stein, and Paolo Rosso (2011).
   Cross-Language Plagiarism Detection.
   In Language Ressources and Evaluation, volume 45, pages 45–62.
   url: http://users.dsic.upv.es/grupos/nle/downloads.html

3. PAN-PC-11
   Martin Potthast, Benno Stein, Alberto Barrón-Cedeño, and Paolo Rosso (2010).
   An Evaluation Framework for Plagiarism Detection.
   In Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), Beijing, China, August 2010. Association for Computational Linguistics.
   url: http://www.uni-weimar.de/en/media/chairs/webis/corpora/pan-pc-11/

4. Webis-CLS-10 (Amazon Product Reviews)
   Peter Prettenhofer and Benno Stein (2010).
   Cross-language text classification using structural correspondence learning.
   In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL), pages 1118-1127.
   url: http://www.uni-weimar.de/en/media/chairs/webis/corpora/corpus-webis-cls-10/

5. TreeTagger
   Helmut Schmid (1994).
   Probabilistic Part-of-Speech Tagging Using Decision Trees.
   In Proceedings of the International Conference on New Methods in Language Processing.
   url: http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/

6. HunAlign
   Dániel Varga, Péter Hálacsy, Viktor Nagy, Lázló Németh, András Kornai, and Viktor Trón (2005).
   Parallel corpora for medium density languages.
   In Recent Advances in Natural Language Processing (RANLP 2005), pages 590–596.
   url: http://mokk.bme.hu/en/resources/hunalign/
   licence: GNU LGPL version 2.1 or later

7. DBNary
   Gilles Sérasset (2014).
   DBnary: Wiktionary as a Lemon-Based Multilingual Lexical Resource in RDF.
   In to appear in Semantic Web Journal (special issue on Multilin- gual Linked Open Data).
   url: http://kaiko.getalp.org/about-dbnary/
   licence: Creative Commons Attribution-ShareAlike 3.0

8. TALN Archives
   Florian Boudin (2013).
   TALN Archives : a digital archive of French research articles in Natural Language Processing (TALN Archives : une archive numérique francophone des articles de recherche en Traitement Automatique de la Langue) [in French]).
   In Proceedings of TALN 2013 (Volume 2: Short Papers), pages 507–514.
   url: https://github.com/boudinfl/taln-archives
   licence: Creative Commons Attribution-NonCommercial 3.0

9. ForceUTF8
   url: https://github.com/neitanod/forceutf8
   licence: BSD

10. Text Language Detect
    url: https://github.com/webmil/text-language-detect
    licence: BSD

11. William B. Cavnar and John M. Trenkle (1994).
    N-Gram-Based Text Categorization.
    In Proceedings of SDAIR- 94, 3rd Annual Symposium on Document Analysis and Information Retrieval, pages 161–175.

12. DBNary PHP Interface
    url: https://github.com/FerreroJeremy/DBNary-PHP-Interface
    licence: Creative Commons Attribution-ShareAlike 4.0 International

Credit

When you use this dataset, please cite:

@inproceedings{CrossLanguageDatasetLREC2016,
  TITLE = {{A Multilingual, Multi-Style and Multi-Granularity Dataset for Cross-Language Textual Similarity Detection}},
  AUTHOR = {J{\'e}r{\'e}my Ferrero and Fr{\'e}d{\'e}ric Agn{\`e}s and Laurent Besacier and Didier Schwab},
  BOOKTITLE = {{The 10th edition of the Language Resources and Evaluation Conference (LREC 2016)}},
  ADDRESS = {Portoro{\v z}, Slovenia},
  YEAR = {2016},
  MONTH = May,
  KEYWORDS = {Cross-language plagiarism detection ; Dataset ; Cross-language dataset ; Cross-language similarity detection ; Evaluation},
}