Cyber Security Event Sentence Detection From News Articles Based on Trigger and Argument

Abstract

Events are critical for comprehending the things that occur in the actual world. The term “events” is frequently used to describe the numerous relationships between people, places, activities, and things. Events-centered modelling entails the representation of several facets of an event in addition to the semantic representation of event facts. Detecting cybersecurity occurrences is important to keep us aware of the rapidly increasing number of such incidents reported via text. The authors focus on cyber security event detection task in this study, specifically on identifying event trigger words and arguments in the cybersecurity area. For this study, they use the CASIE dataset. They propose a system that involves the events identification, event triggers identification, and event arguments extraction. In this section, they divide the cyber security event sentence classification model into two steps: event trigger and argument identification, and cyber security event sentence classification using the training corpus.