Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
This addresses the challenge of low efficiency in capturing long-range dependencies for event extraction in NLP, though it appears incremental as it builds on existing methods.
The paper tackled the problem of extracting multiple events from a single sentence by proposing a JMEE framework that uses syntactic shortcut arcs and attention-based graph convolution networks, achieving competitive results compared to state-of-the-art methods.
Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.