Position-aware end-to-end cross-document event coreference resolution for Dutch

Natural language understanding entails the ability to comprehend the relations between various people, objects or events throughout one, or multiple, text(s). Event coreference resolution (ECR) is a discourse-based natural language processing (NLP) task which aims to link those textual events, be they real or fictional, that refer to the same conceptual event. In this paper, we introduce a novel end-to-end approach for cross-document ECR which combines expert-level positional knowledge and graph-based representations in order to create a memory-efficient and accurate system meant for the detection and resolution of events in large document collections. We make three fundamental architectural changes to a current state-of-the-art cross-document ECR system and show that our approach outperforms this earlier model (+ 4% CONLL F1) on a large Dutch ECR dataset. Moreover, we show through in-depth qualitative and quantitative analysis that our proposed approach consistently detects more relevant events and suffers notably less from the typical issues models exhibit when predicting coreference chains.