A Scalable and Distributed NLP Architecture for Web Document Annotation

  • Julien Deriviere
  • Thierry Hamon
  • Adeline Nazarenko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4139)


In the context of the ALVIS project, which aims at integrating linguistic information in topic-specific search engines, we develop a NLP architecture to linguistically annotate large collections of web documents. This context leads us to face the scalability aspect of Natural Language Processing. The platform can be viewed as a framework using existing NLP tools. We focus on the efficiency of the platform by distributing linguistic processing on several machines. We carry out an an experiment on 55,329 web documents focusing on biology. These 79 million-word collections of web documents have been processed in 3 days on 16 computers.


Entity Recognition Word Segmentation Natural Language Processing Tool Standard Personal Computer Linguistic Annotation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Julien Deriviere
    • 1
  • Thierry Hamon
    • 1
  • Adeline Nazarenko
    • 1
  1. 1.LIPN – UMR CNRS 7030VilletaneuseFrance

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