Most of the morphological properties of derivational Arabic words are encapsulated in their
corresponding morphological patterns. The morphological pattern is a template that shows
how the word should be decomposed into its constituent morphemes (prefix + stem + suffix),
and at the same time, marks the positions of the radicals comprising the root of
the word. The number of morphological patterns in Arabic is finite and is well below 1000.
Due to these properties, most of the current analysis algorithms concentrate on discovering
the morphological pattern of the input word as a major step in recognizing the type and
category of the word. Unfortunately, this process is non-determinitic in the sense that the
underlying search process may sometimes associate more than one morphological pattern
with the given word, all of them satisfying the major lexical constraints. One solution to this
problem is to use a collection of connectionist pattern associaters that uniquely associate
each word with its corresponding morphological pattern. This paper describes an LVQ-based
learning pattern association system that uniquely maps a given Arabic word to its
corresponding morphological pattern, and therefore deduces its morphological properties.
The system consists of a collection of hetroassociative models that are trained using the
LVQ algorithm plus a collection of autoassociative models that have been trained using
backpropagation. Experimental results have shown that the system is fairly accurate and very
easy to train. The LVQ algorithm has been chosen because it is very easy to train and the
implied training time is very small compared to that of backpropagation.