Event Extraction from Historical Texts: A New Dataset for Black Rebellions

Abstract

Understanding historical events is necessary for the study of contemporary society, culture, and politics. In this work, we focus on the event extraction task (EE) to detect event trigger words and their arguments in a novel domain of historical texts. In particular, we introduce a new EE dataset for a corpus of nineteenth-century African American newspapers. Our goal is to study the discourse of slave and non-slave African diaspora rebellions published in the periodical press in this period. Our dataset features 5 entity types, 12 event types, and 6 argument roles that concern slavery and black movements between the eighteenth and nineteenth centuries. Historical newspapers present many challenges for existing EE systems, including the evolution of meanings of words and the extensive use of religious discourse in newspapers from this era. Our experiments with current state-of-the-art EE systems and BERT models demonstrate their poor performance over historical texts and call for more robust research efforts in this area.