Event Extraction Alone Is Not Enough

Research output: Contributions to collected editions/worksConference contributionpeer-review

Authors

With the growing amount of online data, distinguishing between similar events and news about them poses a significant challenge for both companies and crisis reaction units. To discriminate event instances, we present Eventist, a silver-standard event instance dataset from news in English, containing 23,304 news headlines from 90 countries covering in total 113 storm-related events between 1st January 2021 and 1st September 2023. Sampled data is validated by two human raters. Additionally, we propose to adopt a sentence-level event representation for modeling media narrative discourse. Finally, we provide two pairwise comparison benchmarks on event deduplication and event temporal ordering, enabling the practicality of event extraction.

Original languageEnglish
Title of host publicationNarrative Extraction From Texts 2024 : Proceedings of Text2Story — Seventh Workshop on Narrative Extraction From Texts held in conjunction with the 46th European Conference on Information Retrieval (ECIR 2024)
EditorsRicardo Campos, Alípio Mário Jorge, Adam Jatowt, Simut Bhatia, Marina Litvak
Number of pages10
Volume3671
Place of PublicationAachen
PublisherRheinisch-Westfaelische Technische Hochschule Aachen
Publication date05.2024
Pages105-114
Publication statusPublished - 05.2024
Event7th Workshop on Narrative Extraction From Texts, Text2Story 2024 - Glasgow, United Kingdom
Duration: 24.03.202424.03.2024
Conference number: 7
https://text2story24.inesctec.pt/

Bibliographical note

Funding Information:
The authors acknowledge the financial support by the Federal Ministry for Economic Affairs and Energy of Germany in the project CoyPu (project number 01MK21007G).

Publisher Copyright:
© 2024 Copyright for this paper by its authors.

    Research areas

  • Event Deduplication, Event Temporal Ordering, Media Narrative Discourse
  • Informatics