License: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license (CC BY-NC-ND 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.1.4.65
URN: urn:nbn:de:0030-drops-31987
URL: https://drops.dagstuhl.de/opus/volltexte/2011/3198/
Go back to Dagstuhl Reports


Cunningham, Hamish ; Fuhr, Norbert ; Stein, Benno M.
Weitere Beteiligte (Hrsg. etc.): Hamish Cunningham and Norbert Fuhr and Benno M. Stein

Challenges in Document Mining (Dagstuhl Seminar 11171)

pdf-format:
dagrep_v001_i004_p065_s11171.pdf (2 MB)


Abstract

This report documents the programme and outcomes of the Dagstuhl Seminar 11171
"Challenges in Document Mining". Our starting point was the observation
that document mining techniques are often applied in an isolated manner, with
the consequence that their potential is still to be fully realised. The goal
of the seminar was to analyze this untapped potential. To this end researchers
from the main areas of document mining were invited to present their views, to
synthesise an understanding of where and how the latest disciplinary
achievements can be combined, and to develop a more integrative view on the
state of the art and the prospects for future progress.

BibTeX - Entry

@Article{cunningham_et_al:DR:2011:3198,
  author =	{Hamish Cunningham and Norbert Fuhr and Benno M. Stein},
  title =	{{Challenges in Document Mining (Dagstuhl Seminar 11171)}},
  pages =	{65--99},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2011},
  volume =	{1},
  number =	{4},
  editor =	{Hamish Cunningham and Norbert Fuhr and Benno M. Stein},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2011/3198},
  URN =		{urn:nbn:de:0030-drops-31987},
  doi =		{10.4230/DagRep.1.4.65},
  annote =	{Keywords: Cluster analysis, HCI, Retrieval models, Social mining and search, Semi-supervised learning}
}

Keywords: Cluster analysis, HCI, Retrieval models, Social mining and search, Semi-supervised learning
Collection: Dagstuhl Reports, Volume 1, Issue 4
Issue Date: 2011
Date of publication: 12.08.2011


DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI