A system for creating and manipulating generalized wordclass transition matrices from large labelled text-corpora

This paper deals with the training phase of a Markov-type linguistic model that is based on transition probabilities between pvirs and triplets of syntactic categories. To determine the o?timal level of detail for a set of syntactic classes we developed a systetn that uses a set-theoretical formalism to defiue such sets mid has some measm~s to comp~uce and c,ptimize them fildividually. In section two we describe the optimizafiou problem (hi terms of piediction, infoimation and economy requilements) and our approach to its solution. Section three introduces the system dlat will assist a lhlguist in h,'mdling the prediction and economy criteria and in the last section we plesent some slunple lemtlts that can be achieved with it.