Exploring Phonotactics with Simple Recurrent Networks

Learning Lexical Phonotactics With Simple Recurrent Networks

Modelling the phonotactic structure of natural language words with Simple Recurrent Networks

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. Th...

Connectionist Learning of Natural Language Lexical Phonotactics

Connectionist learning of natural language words and their phonetic regularities is presented. The Neural Network (NN) model we employ in this problem is the Simple Recurrent Network, trained with the Backpropagation Through Time (BPTT) learning algorithm. During the training, it was assigned the task of predicting the next phoneme given one phoneme at each moment and keeping information of the...

Solving Linear Semi-Infinite Programming Problems Using Recurrent Neural Networks

‎Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints‎. ‎In this paper‎, ‎to solve this problem‎, ‎we combine a discretization method and a neural network method‎. ‎By a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem‎. ‎Then‎, ‎we use...

An FGREP Investigation into Phonotactics

We discuss experiments with neural networks being trained in a phonotactic processing task. A recurrent network not only learns to predict the next letter given a partial processed word, but also learns to represent the letters in a manner meaningful to the processing task. To this end, we use Miikkulainen’s (1993) FGREP, augmented with an algorithm we call dispersion, to improve distinctness a...