License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.12.5.37
URN: urn:nbn:de:0030-drops-174432
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/17443/
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Collins, Christopher ; Fokkens, Antske ; Kerren, Andreas ; Weaver, Chris ; Chatzimparmpas, Angelos
Weitere Beteiligte (Hrsg. etc.): Christopher Collins and Antske Fokkens and Andreas Kerren and Chris Weaver and Angelos Chatzimparmpas

Visual Text Analytics (Dagstuhl Seminar 22191)

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dagrep_v012_i005_p037_22191.pdf (1 MB)

Abstract

Text data is one of the most abundant types of data available, produced every day across all domains of society. Understanding the contents of this data can support important policy decisions, help us understand society and culture, and improve business processes. While machine learning techniques are growing in their power for analyzing text data, there is still a clear role for human analysis and decision-making. This seminar explored the use of visual analytics applied to text data as a means to bridge the complementary strengths of people and computers. The field of visual text analytics applies visualization and interaction approaches which are tightly coupled to natural language processing systems to create analysis processes and systems for examining text and multimedia data. During the seminar, interdisciplinary working groups of experts from visualization, natural language processing, and machine learning examined seven topic areas to reflect on the state of the field, identify gaps in knowledge, and create an agenda for future cross-disciplinary research. This report documents the program and the outcomes of Dagstuhl Seminar 22191 "Visual Text Analytics".

BibTeX - Entry

@Article{collins_et_al:DagRep.12.5.37,
  author =	{Collins, Christopher and Fokkens, Antske and Kerren, Andreas and Weaver, Chris and Chatzimparmpas, Angelos},
  title =	{{Visual Text Analytics (Dagstuhl Seminar 22191)}},
  pages =	{37--91},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{12},
  number =	{5},
  editor =	{Collins, Christopher and Fokkens, Antske and Kerren, Andreas and Weaver, Chris and Chatzimparmpas, Angelos},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/17443},
  URN =		{urn:nbn:de:0030-drops-174432},
  doi =		{10.4230/DagRep.12.5.37},
  annote =	{Keywords: Information visualization, visual text analytics, visual analytics, text visualization, explainable ML for text analytics, language models, text mining, natural language processing}
}

Keywords: Information visualization, visual text analytics, visual analytics, text visualization, explainable ML for text analytics, language models, text mining, natural language processing
Collection: DagRep, Volume 12, Issue 5
Issue Date: 2022
Date of publication: 08.12.2022

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