Generating Semantic Graphs through Self-Organization

In this study, a technique called semantic self-organization is used to scale up the subsymbolic approach by allowing a network to optimally allocate frame representations from a semantic dependency graph. The resulting architecture, INSOMNet, was trained on semantic representations of the newly-released LinGO Redwoods HPSG Treebank of annotated sentences from the VerbMobil project. The results show that INSOMNet is able to accurately represent the semantic dependencies while demonstrating expectations and defaults, coactivation of multiple interpretations, and robust processing of noisy input. The cognitive plausibility of the model is underscored by the collective modelling of four experiments from the visual worlds paradigm to show the model’s ability to adapt to context.