Abstract
We present a novel approach to unsupervised temporal sequence proc- essing in the form of an unsupervised, recurrent neural network based on a self- organizing map (SOM). A standard SOM clusters each input vector irrespective of context, whereas the recurrent SOM presented here clusters each input based on an input vector and a context vector. The latter acts as a recurrent conduit feeding back a 2-D representation of the previous winning neuron. This recurrency allows the network to operate on temporal sequence processing tasks. The network has been applied to the difficult natural language processing problem of position vari- ant recognition, e.g. recognising a noun phrase regardless of its position within a sentence.
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