ASCUE: An Adversarial Network-Based Semantical Conformance Checking Method for Unsupervised Event Extraction in Social Internet of Things

TL;DRAbstract

Abstract Event extraction (EE) methods are widely used in the Social Internet of Things (SIoT) to help objects obtain the key information from messages shared by other objects. Existing supervised EE methods can only extract predefined events and can hardly extract events with unseen event types. To address this issue, we propose an unsupervised EE method based on the idea of semantic role labeling. However, after extracting all possible events from a given message text, some of these events face the semantically inconsistent issue that will destroy the information credibility in SIoT. To solve this issue, we present ASCUE, an adversarial network-based semantic conformance checking method for unsupervised EE in SIoT. Briefly, ASCUE first mines all the event candidates from a given text unsupervisedly. Next, ASCUE introduces two independent Bidirectional Encoder Representations from Transformers models to capture the semantics of the event candidate and text, respectively. Moreover, to

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