Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures

(1, 2) , (1) , (1)
1
2

Abstract

Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.
Fichier principal
Vignette du fichier
Asynchronous_Pipeline_for_Processing_Huge_Corpora_on_Medium_to_Low_Resource_Infrastructures.pdf (187.31 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02148693 , version 1 (13-07-2019)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Pedro Javier Ortiz Suárez, Benoît Sagot, Laurent Romary. Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures. 7th Workshop on the Challenges in the Management of Large Corpora (CMLC-7), Jul 2019, Cardiff, United Kingdom. ⟨10.14618/IDS-PUB-9021⟩. ⟨hal-02148693⟩
3119 View
1325 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More