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Hermes: A Distributed-Messaging Tool for NLP

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Machine Learning, Optimization, and Big Data (MOD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10122))

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Abstract

In this paper we present Hermes, a novel tool for natural language processing. By employing an efficient and extendable distributed-message architecture, Hermes is able to fullfil the requirements of large-scale processing, completeness, and versatility that are currently missed by existing NLP tools.

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Notes

  1. 1.

    In Greek mythology Hermes is the messenger of the gods. This is an allusion to our distributed-messaging architecture.

  2. 2.

    http://kafka.apache.org.

  3. 3.

    http://camel.apache.org/mail.html.

  4. 4.

    https://github.com/GravityLabs/goose/wiki.

  5. 5.

    http://akka.io.

  6. 6.

    https://www.elastic.co/, https://hbase.apache.org/.

  7. 7.

    http://spark.apache.org/streaming/.

  8. 8.

    http://spark.apache.org/mllib/.

  9. 9.

    https://iptc.org/metadata/.

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Authors and Affiliations

  1. R&D Department, UniCredit, Milan, Italy

    Ilaria Bordino, Andrea Ferretti, Marco Firrincieli, Francesco Gullo, Marcello Paris & Gianluca Sabena

Authors
  1. Ilaria Bordino
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  2. Andrea Ferretti
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  3. Marco Firrincieli
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  4. Francesco Gullo
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  5. Marcello Paris
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  6. Gianluca Sabena
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Corresponding author

Correspondence to Francesco Gullo .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. Semantic Technology Laboratory, National Research Council (CNR), Catania, Italy

    Piero Conca

  3. Dipartimento di Sociologia e Metodi della Ricerca Sociale, Università di Catania, Catania, Italy

    Giovanni Giuffrida

  4. Department of Mathematics and Computer Science, University of Catania, Catania, Italy

    Giuseppe Nicosia

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Bordino, I., Ferretti, A., Firrincieli, M., Gullo, F., Paris, M., Sabena, G. (2016). Hermes: A Distributed-Messaging Tool for NLP. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_33

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  • DOI: https://doi.org/10.1007/978-3-319-51469-7_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51468-0

  • Online ISBN: 978-3-319-51469-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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