TY  - CHAP
A1  - Jannidis, Fotis
A1  - Reger, Isabella
A1  - Weimer, Lukas
A1  - Krug, Markus
A1  - Puppe, Frank
T1  - Automatische Erkennung von Figuren in deutschsprachigen Romanen
N2  - Eine wichtige Grundlage für die quantitative Analyse von Erzähltexten, etwa eine Netzwerkanalyse der Figurenkonstellation, ist die automatische Erkennung von Referenzen auf Figuren in Erzähltexten, ein Sonderfall des generischen NLP-Problems der Named Entity Recognition. Bestehende, auf Zeitungstexten trainierte Modelle sind für literarische Texte nur eingeschränkt brauchbar, da die Einbeziehung von Appellativen in die Named Entity-Definition und deren häufige Verwendung in Romantexten zu einem schlechten Ergebnis führt. Dieses Paper stellt eine anhand eines manuell annotierten Korpus auf deutschsprachige Romane des 19. Jahrhunderts angepasste NER-Komponente vor.
KW  - Digital Humanities
KW  - Figurenerkennung
KW  - Named-Entity-Recognition
KW  - Domänenadaption
KW  - Literatur
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-143332
UR  - https://dhd2015.uni-graz.at/
ER  - 
TY  - JOUR
A1  - Kempf, Sebastian
A1  - Krug, Markus
A1  - Puppe, Frank
T1  - KIETA: Key-insight extraction from scientific tables
JF  - Applied Intelligence
N2  - 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.
KW  - table extraction
KW  - table understanding
KW  - ontology
KW  - key-insight extraction
KW  - information extraction
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324180
SN  - 0924-669X
VL  - 53
IS  - 8
ER  -