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Experiences with parallelisation of an existing NLP pipeline : tagging Hansard

Wattam, Stephen and Rayson, Paul and Alexander, Marc and Anderson, Jean (2014) Experiences with parallelisation of an existing NLP pipeline : tagging Hansard. In: LREC 2014, Ninth International Conference on Language Resources and Evaluation :. EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA, Paris, pp. 4093-4096. ISBN 9782951740884

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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

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: High-performance Computing ; Parallelisation ; Tagging
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
Faculty of Science and Technology > Mathematics and Statistics
ID Code: 76443
Deposited By: ep_importer_pure
Deposited On: 02 Nov 2015 14:20
Refereed?: No
Published?: Published
Last Modified: 10 Oct 2017 20:16
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/76443

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