Neural Network Model for Word Sense Disambiguation using Up/Down State and Morphoelectrotonic Transform

Abstract

We propose a neural network model for word sense disambiguation using up and down state neurons and morphoelectrotonic transform. Relations between stimulus words and associated words are implemented on this neural network by using an associative ontology. This new neural coding model enables word sense disambiguation in an input sentence by using firing dynamics on the neural network. It is decided whether to put a new link between two neurons by using a co-occurrence frequency between two words corresponding the neurons and an attenuation rate of morphoelectrotonic potential between the two neuron. The distance of the new link is obtained by learning from calculating the morphoelectrotonic transform from the two neuron's morphoelectrotonic potential. It is shown that this model has a small-world structure by analyzing the learning behavior using average shortest path lengths and clustering coefficients.