@inproceedings{JannidisRegerWeimeretal.2015,
  author    = {Jannidis, Fotis and Reger, Isabella and Weimer, Lukas and Krug, Markus and Puppe, Frank},
  title     = {Automatische Erkennung von Figuren in deutschsprachigen Romanen},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-143332},
  pages     = {7},
  year      = {2015},
  abstract  = {Eine wichtige Grundlage f{\"u}r die quantitative Analyse von Erz{\"a}hltexten, etwa eine Netzwerkanalyse der Figurenkonstellation, ist die automatische Erkennung von Referenzen auf Figuren in Erz{\"a}hltexten, ein Sonderfall des generischen NLP-Problems der Named Entity Recognition. Bestehende, auf Zeitungstexten trainierte Modelle sind f{\"u}r literarische Texte nur eingeschr{\"a}nkt brauchbar, da die Einbeziehung von Appellativen in die Named Entity-Definition und deren h{\"a}ufige Verwendung in Romantexten zu einem schlechten Ergebnis f{\"u}hrt. Dieses Paper stellt eine anhand eines manuell annotierten Korpus auf deutschsprachige Romane des 19. Jahrhunderts angepasste NER-Komponente vor.},
  subject      = {Digital Humanities},
  language  = {de}
}
@article{KempfKrugPuppe2023,
  author    = {Kempf, Sebastian and Krug, Markus and Puppe, Frank},
  title     = {KIETA: Key-insight extraction from scientific tables},
  series = {Applied Intelligence},
  volume    = {53},
  journal   = {Applied Intelligence},
  number    = {8},
  issn      = {0924-669X},
  doi       = {10.1007/s10489-022-03957-8},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324180},
  pages     = {9513-9530},
  year      = {2023},
  abstract  = {An important but very time consuming part of the research process is literature review. An already large and nevertheless growing ground set of publications as well as a steadily increasing publication rate continue to worsen the situation. Consequently, automating this task as far as possible is desirable. Experimental results of systems are key-insights of high importance during literature review and usually represented in form of tables. Our pipeline KIETA exploits these tables to contribute to the endeavor of automation by extracting them and their contained knowledge from scientific publications. The pipeline is split into multiple steps to guarantee modularity as well as analyzability, and agnosticim regarding the specific scientific domain up until the knowledge extraction step, which is based upon an ontology. Additionally, a dataset of corresponding articles has been manually annotated with information regarding table and knowledge extraction. Experiments show promising results that signal the possibility of an automated system, while also indicating limits of extracting knowledge from tables without any context.},
  language  = {en}
}