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Interactive Data Analytics for the Humanities

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Computational Linguistics and Intelligent Text Processing (CICLing 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10761))

Abstract

In this vision paper, we argue that current solutions to data analytics are not suitable for complex tasks from the humanities, as they are agnostic of the user and focused on static, predefined tasks with large-scale benchmarks. Instead, we believe that the human must be put into the loop to address small data scenarios that require expert domain knowledge and fluid, incrementally defined tasks, which are common for many humanities use cases. Besides the main challenges, we discuss existing and urgently required solutions to interactive data acquisition, model development, model interpretation, and system support for interactive data analytics. In the envisioned interactive systems, human users not only provide annotations to a machine learner, but train a model by using the system and demonstrating the task. The learning system will actively query the user for feedback, refine its model in real-time, and is able to explain its decisions. Our vision links natural language processing research with recent advances in machine learning, computer vision, and data management systems, as realizing this vision relies on combining expertise from all of these scientific fields.

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Notes

  1. 1.

    Entities should not be multiplied beyond necessity.

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Authors and Affiliations

  1. Department of Computer Science, Technische Universität Darmstadt, Darmstadt, Germany

    Iryna Gurevych, Christian M. Meyer, Carsten Binnig, Johannes Fürnkranz, Kristian Kersting, Stefan Roth & Edwin Simpson

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  1. Iryna Gurevych
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  2. Christian M. Meyer
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Correspondence to Iryna Gurevych or Christian M. Meyer .

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  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Gurevych, I. et al. (2018). Interactive Data Analytics for the Humanities. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_41

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  • DOI: https://doi.org/10.1007/978-3-319-77113-7_41

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