Fig. 2
From: Joint contrastive learning and belief rule base for named entity recognition in cybersecurity
The contrastive learning objectives. In the mini-batch, entities such as Stuxnet, WannaCry, Mirai Botnet, and NotPetya are predefined “malware” entities. In the vector space, for “malware” entities, we define the prototype of corresponding entity representations as anchors, denoted by cross marks. Positive samples are representations of “malware” entities, indicated by green triangles, while negative samples are representations of other token sequences, represented by blue circles