Kavli Affiliate: Lihong Wang
| First 5 Authors: Qian Li, Shu Guo, Jia Wu, Jianxin Li, Jiawei Sheng
| Summary:
Event extraction (EE), which acquires structural event knowledge from texts,
can be divided into two sub-tasks: event type classification and element
extraction (namely identifying triggers and arguments under different role
patterns). As different event types always own distinct extraction schemas
(i.e., role patterns), previous work on EE usually follows an isolated learning
paradigm, performing element extraction independently for different event
types. It ignores meaningful associations among event types and argument roles,
leading to relatively poor performance for less frequent types/roles. This
paper proposes a novel neural association framework for the EE task. Given a
document, it first performs type classification via constructing a
document-level graph to associate sentence nodes of different types, and
adopting a graph attention network to learn sentence embeddings. Then, element
extraction is achieved by building a universal schema of argument roles, with a
parameter inheritance mechanism to enhance role preference for extracted
elements. As such, our model takes into account type and role associations
during EE, enabling implicit information sharing among them. Experimental
results show that our approach consistently outperforms most state-of-the-art
EE methods in both sub-tasks. Particularly, for types/roles with less training
data, the performance is superior to the existing methods.
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