Event extraction by associating event types and argument roles

Qian Li, Shu Guo, Jia Wu, Jianxin Li*, Jiawei Sheng, Hao Peng, Lihong Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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 event graph to associate sentence nodes of different types and adopting a document-awared graph attention network to learn sentence embeddings. Then, element extraction is achieved by building a new schema of argument roles, with a type-awared 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, especially at least 2.51% and 1.12% improvement of the event trigger identification and argument role classification sub-tasks. Particularly, for types/roles with less training data, the performance is superior to the existing methods.

Original languageEnglish
Pages (from-to)1549-1560
Number of pages12
JournalIEEE Transactions on Big Data
Issue number6
Publication statusPublished - Dec 2023


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