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CATI: An Extensible Platform Supporting Assisted Classification of Large Datasets

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Web Information Systems and Technologies (WEBIST 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 399))

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Abstract

More and more, researchers in humanities and companies need large classified document data-sets. These users are not familiar with information retrieval or data science notions. For data scientists, there is also often a need for those classified document data-sets as ground truth. There are multiple tools that allow users to carry out this classification task on large data-sets, involving always a quite expert level in computer and data science. More over, these tools are not usually oriented to the domain of micro-blogs or do not always take into account meta data and attached images as additional dimensions to improve the classification. In this work, we present a platform to enable end users to classify large document collections of several hundred thousands documents in an assisted way, within a humanly acceptable number of clicks, with no coding and without having data science and information retrieval expert knowledge. The system includes a graphical user interface with several classification assistants doing text- and image-based event detection, geographical filtering, image clustering, search services with rich visual metaphors to visualize their results and finally Active Learning (AL) with different sampling strategies. We also present a comparative study on the impact of using different and interchangeable AL components on the number of clicks needed to reach a stable level of accuracy.

Supported by LABEX IMU under the project IDENUM: Identitées numériques urbaines. http://imu.universite-lyon.fr/projet/idenum-identites-numeriques-urbaines.

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Notes

  1. 1.

    https://opennlp.apache.org.

  2. 2.

    http://imu.universite-lyon.fr/projet/idenum-identites-numeriques-urbaines.

  3. 3.

    CATI’s documentation, videos and source code: https://bitbucket.org/idenum/cati/wiki/Home.

  4. 4.

    https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/28075.

  5. 5.

    https://monkeylearn.com/.

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Authors and Affiliations

  1. Université de Lyon, LIRIS UMR 5205 CNRS, Bâtiment Blaise Pascal, 20 Avenue Albert Einstein, 69621, Villeurbanne, France

    Gabriela Bosetti & Előd Egyed-Zsigmond

Authors
  1. Gabriela Bosetti
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  2. Előd Egyed-Zsigmond
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Corresponding author

Correspondence to Előd Egyed-Zsigmond .

Editor information

Editors and Affiliations

  1. Delft University of Technology, Delft, The Netherlands

    Alessandro Bozzon

  2. University of Seville, Sevilla, Sevilla, Spain

    Francisco José Domínguez Mayo

  3. Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Bosetti, G., Egyed-Zsigmond, E. (2020). CATI: An Extensible Platform Supporting Assisted Classification of Large Datasets. In: Bozzon, A., Domínguez Mayo, F.J., Filipe, J. (eds) Web Information Systems and Technologies. WEBIST 2019. Lecture Notes in Business Information Processing, vol 399. Springer, Cham. https://doi.org/10.1007/978-3-030-61750-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-61750-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61749-3

  • Online ISBN: 978-3-030-61750-9

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