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Interactive Data Analytics for the Humanities

Gurevych, Iryna and Meyer, Christian M. and Binnig, Carsten and Fürnkranz, Johannes and Kersting, Kristian and Roth, Stefan and Simpson, Edwin
Gelbukh, Alexander (ed.) (2018):
Interactive Data Analytics for the Humanities.
In: Computational Linguistics and Intelligent Text Processing: Proceedings of the 18th International Conference, Berlin/Heidelberg, Springer, pp. 527-549, DOI: 10.1007/978-3-319-77113-7_41,
[Online-Edition: https://link.springer.com/chapter/10.1007%2F978-3-319-77113-...],
[Book Section]

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.

Item Type: Book Section
Erschienen: 2018
Editors: Gelbukh, Alexander
Creators: Gurevych, Iryna and Meyer, Christian M. and Binnig, Carsten and Fürnkranz, Johannes and Kersting, Kristian and Roth, Stefan and Simpson, Edwin
Title: Interactive Data Analytics for the Humanities
Language: English
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.

Title of Book: Computational Linguistics and Intelligent Text Processing: Proceedings of the 18th International Conference
Series Name: Lecture Notes in Computer Science
Volume: 10761
Place of Publication: Berlin/Heidelberg
Publisher: Springer
ISBN: 978-3-319-77112-0
Uncontrolled Keywords: invited;UKP_invited
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Date Deposited: 21 Mar 2017 16:16
DOI: 10.1007/978-3-319-77113-7_41
Official URL: https://link.springer.com/chapter/10.1007%2F978-3-319-77113-...
Identification Number: TUD-CS-2017-0067
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