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A scalable architecture for data-intensive natural language processing†

Published online by Cambridge University Press:  09 May 2017

ZUHAITZ BELOKI Open the ORCID record for ZUHAITZ BELOKI [Opens in a new window] ,
XABIER ARTOLA  and
AITOR SOROA
ZUHAITZ BELOKI
Affiliation:
IXA NLP Group, University of the Basque Country (UPV/EHU), Donostia-San Sebastián e-mail: zuhaitz.beloki@ehu.eus, xabier.artola@ehu.eus, a.soroa@ehu.eus
XABIER ARTOLA
Affiliation:
IXA NLP Group, University of the Basque Country (UPV/EHU), Donostia-San Sebastián e-mail: zuhaitz.beloki@ehu.eus, xabier.artola@ehu.eus, a.soroa@ehu.eus
AITOR SOROA
Affiliation:
IXA NLP Group, University of the Basque Country (UPV/EHU), Donostia-San Sebastián e-mail: zuhaitz.beloki@ehu.eus, xabier.artola@ehu.eus, a.soroa@ehu.eus
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Abstract

Computational power needs have greatly increased during the last years, and this is also the case in the Natural Language Processing (NLP) area, where thousands of documents must be processed, i.e., linguistically analyzed, in a reasonable time frame. These computing needs have implied a radical change in the computing architectures and big-scale text processing techniques used in NLP. In this paper, we present a scalable architecture for distributed language processing. The architecture uses Storm to combine diverse NLP modules into a processing chain, which carries out the linguistic analysis of documents. Scalability requires designing solutions that are able to run distributed programs in parallel and across large machine clusters. Using the architecture presented here, it is possible to integrate a set of third-party NLP modules into a unique processing chain which can be deployed onto a distributed environment, i.e., a cluster of machines, so allowing the language-processing modules run in parallel. No restrictions are placed a priori on the NLP modules apart of being able to consume and produce linguistic annotations following a given format. We show the feasibility of our approach by integrating two linguistic processing chains for English and Spanish. Moreover, we provide several scripts that allow building from scratch a whole distributed architecture that can be then easily installed and deployed onto a cluster of machines. The scripts and the NLP modules used in the paper are publicly available and distributed under free licenses. In the paper, we also describe a series of experiments carried out in the context of the NewsReader project with the goal of testing how the system behaves in different scenarios.

Type
Articles
Information
Natural Language Engineering , Volume 23 , Issue 5 , September 2017 , pp. 709 - 731
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

†

This work has been partially funded by the NewsReader (FP7-ICT-2011-8-316404) project. Zuhaitz Beloki’s work is funded by a PhD grant from the University of the Basque Country.

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