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Interactive Data Analytics for the Humanities

Gurevych, Iryna ; Meyer, Christian M. ; Binnig, Carsten ; Fürnkranz, Johannes ; Kersting, Kristian ; Roth, Stefan ; Simpson, Edwin
Gelbukh, Alexander (ed.) :

Interactive Data Analytics for the Humanities.
[Online-Edition: https://link.springer.com/chapter/10.1007%2F978-3-319-77113-...]
In: Computational Linguistics and Intelligent Text Processing: Proceedings of the 18th International Conference. Lecture Notes in Computer Science, 10761. Springer, Berlin/Heidelberg , S. 527-549. ISBN 978-3-319-77112-0
[Buchkapitel] , (2018)

Offizielle URL: https://link.springer.com/chapter/10.1007%2F978-3-319-77113-...

Kurzbeschreibung (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.

Typ des Eintrags: Buchkapitel
Erschienen: 2018
Herausgeber: Gelbukh, Alexander
Autor(en): Gurevych, Iryna ; Meyer, Christian M. ; Binnig, Carsten ; Fürnkranz, Johannes ; Kersting, Kristian ; Roth, Stefan ; Simpson, Edwin
Titel: Interactive Data Analytics for the Humanities
Sprache: Englisch
Kurzbeschreibung (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.

Buchtitel: Computational Linguistics and Intelligent Text Processing: Proceedings of the 18th International Conference
Reihe: Lecture Notes in Computer Science
Band: 10761
Ort: Berlin/Heidelberg
Verlag: Springer
Freie Schlagworte: invited;UKP_invited
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen
Hinterlegungsdatum: 21 Mär 2017 16:16
DOI: 10.1007/978-3-319-77113-7_41
Offizielle URL: https://link.springer.com/chapter/10.1007%2F978-3-319-77113-...
ID-Nummer: TUD-CS-2017-0067
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