Experimenting with Unsupervised Multilingual Event Detection in Historical Newspapers

Abstract

To prevent historical knowledge's fading, research in event detection could facilitate access to digitized collections. In this paper, we propose a method for annotating multilingual historical documents for event detection in an unsupervised manner by leveraging entities and semantic notions of event types. We automatically annotate the documents by relying on dependency parse trees and automatic semantic mapping to event-based frames, with a focus on the multilingual transfer between frames and candidate events. The documents are afterward verified by native speakers, Digital Humanities researchers. We also report on experimental results of event detection in historical newspapers with a state-of-the-art model. We demonstrate that our approach allows for easy language adaptation by presenting two study cases with knowledge extracted from German newspapers from 1911 to 1933 regarding events surrounding International Women's Day and from French newspapers between 1900 and 1944 related to the abolition of guillotine executions in France. Our preliminary findings show that this type of approach could alleviate the need for manual annotation by also providing a practical course of action toward unsupervised event detection from multilingual digitized and historical documents.