Event-Aware Document-Level Event Extraction via Multi-granularity Event Encoder

Event extraction (EE) is a crucial task in natural language processing that entails identifying and extracting events from unstructured text. However, the prior research has largely concentrated on sentence-level event extraction (SEE), while disregarding the increasing requirements for document-level event extraction (DEE) in real-world scenarios. The latter presents two significant challenges, namely the arguments scattering problem and the multi-event problem, which are more frequently observed in documents. In this paper, we propose an event-aware document-level event extraction framework, which can accurately detect event locations throughout the entire document without triggers and encode information at three different granularities (i.e., event-level, document-level, and sentence-level) via a multi-granularity event encoder. The resulting event-related holistic representation is then utilized for subsequent event record generation, thereby improving the accuracy of argument classification. Our proposed model’s effectiveness is demonstrated through experimental results obtained from a large Chinese financial dataset.