[Sigmetrics] Topic extraction challenge

[Andreas Strotmann]

Actually, this would be much more useful if we had the full text of these
articles, too.  There is a lot of research going on with respect to in-text
citation/citation context analysis these days.  That research would benefit
greatly from such an opportunity for comparisons (as would be the efforts
to improve existing citation databases, I suspect.)

On Tue, Mar 14, 2017 at 8:35 AM, Andrea Scharnhorst <
andrea.scharnhorst at dans.knaw.nl> wrote:

> Please take apologies for cross-posting
>
> Dear colleagues,
>
> You might be interested to learn that Clarivate Analytics has agreed to
> make available a Web of Science data set (111,616 articles in Astrophysics
> and Astronomy, "Astro Data Set") which allows us to invite everybody
> interested to participate in a topic extraction challenge so we can
> collectively study and learn from the systematic comparison of topic
> extraction approaches. For details about the challenge and how to access
> the data see: www.topic-challenge.info.
>
> The activity of exploring differences between topic extraction approaches
> originated from discussions at the annual advisory board meeting of a
> project on measuring the epistemic diversity of research by Frank Havemann,
> Michael Heinz and Jochen Gläser in Berlin, Germany, and is documented in a
> special issue "Same Data, Different Results?" of the journal Scientometrics
> (see list of forthcoming articles below).
>
> We would like to invite you and your colleagues to participate in the
> topic extraction challenge, and would appreciate your support in  spreading
> the news. A first opportunity to discuss new topic extraction solutions and
> the challenge of comparing approaches will be provided this October at a
> special session at the 2017 ISSI conference in Wuhan, China (
> http://www.issi2017.org). Submission deadline for research in progress
> papers is April 10. You are welcome to forward this invitation to others
> who may have an interest in topic extraction from the Astro Data Set.
> Please send questions to theresa.velden at gmail.com.
>
> With kind regards,
>
> Theresa Velden
> (& the other "challengers": Kevin Boyack, Wolfgang Glänzel, Jochen Gläser,
> Frank Havemann, Andrea Scharnhorst, Bart Thijs, Nees Jan van Eck, Ludo
> Waltman)
>
>
>
>
> List of forthcoming Scientometrics articles on topic extraction from the
> Astro Data Set:
> ------------------------------------------------------------
> ----------------------------------------------------
>
> Gläser J, Scharnhorst A & Glänzel, W (eds). Introduction: Same data –
> different results? Towards a comparative approach to the identification of
> thematic structures in science. DOI: 10.1007/s11192-017-2296-z
>
> Boyack K. Investigating the Effect of Global Data on Topic Detection. DOI:
> 10.1007/s11192-017-2297-y
>
> Wang, S. & Koopman, R. Clustering articles based on semantic
> similarity. DOI:10.1007/s11192-017-2298-x
>
> Velden T, Yan S & Lagoze C. Mapping the Cognitive Structure of
> Astrophysics by Infomap Clustering of the Citation Network and Topic
> Affinity Analysis. DOI: 10.1007/s11192-017-2299-9
>
> Van Eck, NJ & Waltman, L. Citation-Based Clustering of Publications Using
> CitNetExplorer and VOSviewer  DOI: 10.1007/s11192-017-2300-7
>
> Glänzel, W & Thijs B. Using Hybrid Methods and `Core Documents' for the
> Representation of Clusters and Topics. The Astronomy Dataset DOI:
> 10.1007/s11192-017-2301-6
>
> Havemann F, Gläser J & Heinz M. Memetic Search for Overlapping Topics
> Based on a Local Evaluation of Link Communities DOI:
> 10.1007/s11192-017-2302-5
>
> Koopman R, Wang S & Scharnhorst A. Contextualization of Topics: Browsing
> through the Universe of Bibliographic Information  DOI:
> 10.1007/s11192-017-2303-4
>
> Boyack, K. Thesaurus-Based Methods for Mapping Contents of Publication
> Sets  DOI: 10.1007/s11192-017-2304-3
>
> Koopman R & Wang S. Mutual Information Based Labelling and Comparing
> Clusters  DOI: 10.1007/s11192-017-2305-2
>
> Velden T, Boyack K, Glaser J, Koopman R, Scharnhorst A & Wang S.
> Comparison of Topic Extraction Approaches and Their Results DOI:
> 10.1007/s11192-017-2299-9 DOI: 10.1007/s11192-017-2306-1
>
>
>
>
> Theresa Velden, PhD
> IPODI/Marie Curie Fellow
> Zentrum für Technik und Gesellschaft
> Technische Universität Berlin
>
> velden at ztg.tu-berlin.de| tav6 at cornell.edu | theresa.velden at gmail.com
>
>
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