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Toward Real Event Detection

Färber, Michael; Rettinger, Achim

Abstract:
News agencies and other news providers or consumers are confronted with the task of extracting events from news articles. This is done i) either to monitor and, hence, to be informed about events of specific kinds over time and/or ii) to react to events immediately. In the past, several promising approaches to extracting events from text have been proposed. Besides purely statistically-based approaches there are methods to represent events in a semantically-structured form, such as graphs containing actions (predicates), participants (entities), etc. However, it turns out to be very dificult to automatically determine whether an event is real or not. In this paper, we give an overview of approaches which proposed solutions for this research problem. We show that there is no gold standard dataset where real events are annotated in text documents in a fine-grained, semantically-enriched way. We present A methodology of creating such a dataset with the help of crowdsourcing and present preliminary results.

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Verlagsausgabe §
DOI: 10.5445/IR/1000092928
Veröffentlicht am 17.04.2019
Coverbild
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Jahr 2015
Sprache Englisch
Identifikator ISSN: 1613-0073
KITopen-ID: 1000092928
Erschienen in 4th International Workshop on Detection, Representation, and Exploitation of Events in the Semantic Web, DeRiVE 2015 - Co-located with the 12th Extended Semantic Web Conference, ESWC 2015; Portoroz; Slovenia; 31 May 2015 through. Ed.: D. A. Shamma
Verlag RWTH, Aachen
Seiten 24-34
Serie CEUR Workshop Proceedings ; 1363
Nachgewiesen in Scopus
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