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Unsupervised Automatic Keyphrases Extraction Algorithms

Experimentations on Paintings

  • Conference paper
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On the Move to Meaningful Internet Systems: OTM 2018 Workshops (OTM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11231))

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Abstract

Massive volumes of images of museums or art collections, or made available by artists and photographers, more and more often, are available on the web, along with some metadata, essential for their characterization and retrieval. A set of (scored) keywords/keyphrases that characterize the semantic content of the documents should be, automatically or manually, extracted and/or associated. We present here a work-in-progress to evaluate different methods for the unsupervised keyword extraction to Italian and English datasets. In the paper datasets, algorithms and approaches are presented and discussed together with some preliminary results referred to relatedness of terms.

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Authors and Affiliations

  1. CNR IMATI – Milano, Via Bassini 15, 20133, Milan, Italy

    Isabella Gagliardi & Maria Teresa Artese

Authors
  1. Isabella Gagliardi
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  2. Maria Teresa Artese
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Corresponding author

Correspondence to Isabella Gagliardi .

Editor information

Editors and Affiliations

  1. Trinity College Dublin, Dublin, Ireland

    Christophe Debruyne

  2. University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

  3. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Wided Guédria

  4. Maastricht University, Maastricht, The Netherlands

    Peter Bollen

  5. University Babes-Bolyai, Cluj-Napoca, Romania

    Ioana Ciuciu

  6. TU Graz, Graz, Austria

    Robert Meersman

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Gagliardi, I., Artese, M.T. (2019). Unsupervised Automatic Keyphrases Extraction Algorithms. In: Debruyne, C., Panetto, H., Guédria, W., Bollen, P., Ciuciu, I., Meersman, R. (eds) On the Move to Meaningful Internet Systems: OTM 2018 Workshops. OTM 2018. Lecture Notes in Computer Science(), vol 11231. Springer, Cham. https://doi.org/10.1007/978-3-030-11683-5_29

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  • DOI: https://doi.org/10.1007/978-3-030-11683-5_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11682-8

  • Online ISBN: 978-3-030-11683-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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