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 events and as triggers of inner 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 and outer events and further recognizes
the PEs via classifying the relation type between trigger pairs. In order to
obtain better representations of triggers and arguments to further improve NEE
performance, it incorporates the information of both event types and argument
roles into PerNee through prompt learning. 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
generic domain and construct a new NEE dataset, namely ACE2005-Nest.
Experimental results demonstrate that PerNee consistently achieves
state-of-the-art performance on ACE2005-Nest, Genia11 and Genia13.

本文提出了一种名为 PerNee 的新模型，它通过识别 Pivot Elements 来提取嵌套事件，以改进现有的 Nested Event Extraction 方法。PerNee 通过 prompt learning 将事件类型和参数角色的信息融入其中，以获得更好的触发器和参数表示，从而在 ACE2005-Nest、Genia11 和 Genia13 数据集上取得了最先进的性能。