Inflation Narratives from a Machine Learning Perspective

135
Not scheduled
20m
Von-Melle-Park 4

Von-Melle-Park 4

Poster

Speakers

Cedric Möller (Universität Hamburg) Junbo Huang (Universität Hamburg)

Description

Inflation narratives explain inflation changes and affect expectations. Manually identifying them is cumbersome, prompting the need for scalable algorithms. Narratives comprise events, causal relations, and arguments, represented 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 subproblems: 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.

Keywords

Inflation Narratives
Event Extraction
Causal Relations Extraction
Knowledge Graphs

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Primary authors

Cedric Möller (Universität Hamburg) Junbo Huang (Universität Hamburg) Max Weinig (Universität Hamburg) Ricardo Usbeck (Leuphana Universität Lüneburg) Ulrich Fritsche (Universität Hamburg)

Presentation materials