Simple Recurrent Networks (SRN) are Neural Network (connectionist) models able to process natural language. Phonotactics concerns the order of symbols in words. We continued an earlier unsuccessful trial to model the phonotactics of Dutch word corpus with SRNs. In order to overcome the previously reported obstacles, a new method for network testing was developed - optimal threshold evaluation. This method is based on minimising the erroneous character prediction of a trained SRN. The network training was improved as well. The training words were presented to the network according to their frequencies, which emphasises the more frequent sequences. The achieved results are promising and provide a base for further study. 1. Introduction to connectionist natural language processing. It is still a challenge to process natural language with connectionist paradigms. Formal language theory provides more natural methods for exploring complex language phenomena, but if we search for an approach...

this reportedly improved the learning. And still another important contribution in this work was a method for representing recursive structures -- by means of symbolic transformation of any tree structure into a binary tree, which can easily be transformed to a sequence. Those two operations are reversible,