Inflation Narratives from a Machine Learning Perspective

Publikation: Beiträge in SammelwerkenAbstracts in KonferenzbändenForschungbegutachtet

Authors

  • Cedric Möller
  • Junbo Huang
  • Max Valentin Weinig
  • Ricardo Usbeck
  • Ulrich Fritsche
Inflation narratives explain inflation changes and affect expectations. Manu- ally identifying them is cumbersome, prompting the need for scalable algo- rithms. Narratives comprise events, causal relations, and arguments, repre- sented as graphs with event and argument nodes. Causal relations indicate cause-and-effect relationships between events using directed edges. Our main objective is to extract narratives from text to enhance a knowledge graph for analysis like social network analysis or edge prediction. We address two sub- problems: event extraction, involving event type and argument identification, and event deduplication. Second, we extract causal relations as expressed by authors, not necessarily true causal links between events in the text.
OriginalspracheEnglisch
TitelDigital Total - Computing & Data Science an der Universität Hamburg und in der Wissenschaftsmetropole Hamburg : Book of Abstracts
HerausgeberMartin Semmann, Seid Muhie Yimam, Katrin Schöning-Stierand, Chris Biemann
Anzahl der Seiten1
ErscheinungsortHamburg
VerlagUniversitat Hamburg
Erscheinungsdatum01.10.2023
Seiten143
PublikationsstatusErschienen - 01.10.2023
VeranstaltungDigital Total - Computing & Data Science an der Universität Hamburg und in der Wissenschaftsmetropole Hamburg
- House of Computing and Data Science - University Hamburg, Hamburg , Deutschland
Dauer: 09.10.202310.10.2023
https://www.hcds.uni-hamburg.de/current/all-events/digital-total.html