Hybrid Approaches to Neural Network-based Language Processing

In this paper we outline hybrid approaches to arti cial neural network-based natural language processing. We start by motivating hybrid symbolic/connectionist processing. Then we suggest various types of symbolic/connectionist integration for language processing: connectionist structure architectures, hybrid transfer architectures, hybrid processing architectures. Furthermore, we focus particularly on loosely coupled, tightly coupled, and fully integrated hybrid processing architectures. We give particular examples of these hybrid processing architectures and argue that the hybrid approach to arti cial neural network-based language processing has a lot of potential to overcome the gap between a neural level and a symbolic conceptual level.