Interactive Data Analytics for the Humanities

Iryna Gurevych*, Christian Meyer*, Carsten Binnig, Furnkranz Johannes, Kristian Kersting, Stefan Roth, Edwin Simpson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

4 Citations (Scopus)
199 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationCICLing 2017
Subtitle of host publicationComputational Linguistics and Intelligent Text Processing
Number of pages28
ISBN (Electronic)978-3-319-77113-7
ISBN (Print)978-3-319-77112-0
Publication statusE-pub ahead of print - 10 Oct 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Nature
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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