Introduction

INTRODUCTION Computational research on natural language has been going on for decades in artificial intelligence and computational linguistics. These disciplines generated enormous excitement in the ’60s and ’70s, but they have not entirely realised their promise and have now reached what seems to be a plateau. Why should connectionist natural language processing (CNLP) be any different? There are a number of reasons. For many, the connectionist approach provides a new way of looking at old issues. For these researchers, connectionism provides an expanded toolkit with which to invigorate old research projects with new ideas. For instance, connectionist systems can learn from examples so that, in the context of a rule-based system, all of the rules need not be specified a priori. Connectionist systems have very powerful generalisation capabilities. Content addressable memory or pattern completion falls naturally out of distributed connectionist systems, making them ideal for filling in missing information.