  Nested Event Extraction (NEE) aims to extract complex event structures where
an event contains other events as its arguments recursively. Nested events
involve a kind of Pivot Elements (PEs) that simultaneously act as arguments of
outer-nest events and as triggers of inner-nest events, and thus connect them
into nested structures. This special characteristic of PEs brings challenges to
existing NEE methods, as they cannot well cope with the dual identities of PEs.
Therefore, this paper proposes a new model, called PerNee, which extracts
nested events mainly based on recognizing PEs. Specifically, PerNee first
recognizes the triggers of both inner-nest and outer-nest events and further
recognizes the PEs via classifying the relation type between trigger pairs. The
model uses prompt learning to incorporate information from both event types and
argument roles for better trigger and argument representations to improve NEE
performance. Since existing NEE datasets (e.g., Genia11) are limited to
specific domains and contain a narrow range of event types with nested
structures, we systematically categorize nested events in the generic domain
and construct a new NEE dataset, called ACE2005-Nest. Experimental results
demonstrate that PerNee consistently achieves state-of-the-art performance on
ACE2005-Nest, Genia11, and Genia13. The ACE2005-Nest dataset and the code of
the PerNee model are available at https://github.com/waysonren/PerNee.
