Home > Research > Publications & Outputs > Experiences with parallelisation of an existing...
View graph of relations

Experiences with parallelisation of an existing NLP pipeline: tagging Hansard

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Close
Publication date2014
Host publicationLREC 2014, Ninth International Conference on Language Resources and Evaluation
EditorsNicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Place of PublicationParis
PublisherEUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
Pages4093-4096
Number of pages4
ISBN (Print)9782951740884
<mark>Original language</mark>English
Event9th International Conference on Language Resources and Evaluation (LREC) - Reykjavik, Iceland
Duration: 26/05/201431/05/2014

Conference

Conference9th International Conference on Language Resources and Evaluation (LREC)
Country/TerritoryIceland
Period26/05/1431/05/14

Conference

Conference9th International Conference on Language Resources and Evaluation (LREC)
Country/TerritoryIceland
Period26/05/1431/05/14

Abstract

This poster describes experiences processing the two-billion-word Hansard corpus using a fairly standard NLP pipeline on a high performance cluster. Herein we report how we were able to parallelise and apply a "traditional" single-threaded batch-oriented application to a platform that differs greatly from that for which it was originally designed. We start by discussing the tagging toolchain, its specific requirements and properties, and its performance characteristics. This is contrasted with a description of the cluster on which it was to run, and specific limitations are discussed such as the overhead of using SAN-based storage. We then go on to discuss the nature of the Hansard corpus, and describe which properties of this corpus in particular prove challenging for use on the system architecture used. The solution for tagging the corpus is then described, along with performance comparisons against a naive run on commodity hardware. We discuss the gains and benefits of using high-performance machinery rather than relatively cheap commodity hardware. Our poster provides a valuable scenario for large scale NLP pipelines and lessons learnt from the experience