Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification

arXiv:2403.03305v1 Announce Type: new
Abstract: This paper introduces a novel neuro-symbolic architecture for relation classification (RC) that combines rule-based methods with contemporary deep learning techniques. This approach capitalizes on the strengths of both paradigms: the adaptability of rule-based systems and the generalization power of neural networks. Our architecture consists of two components: a declarative rule-based model for transparent classification and a neural component to enhance rule generalizability through semantic text matching. Notably, our semantic matcher is trained in an unsupervised …

abstract adaptability architecture arxiv best of classification cs.ai cs.cl deep learning deep learning techniques networks neural networks neuro novel paper power systems type