Introduction of Rules into a Stochastic Approach for Language Modelling

Summary

Automatic morpho-syntactic tagging is an area where statistical approaches have been more successful than rule-based methods. Nevertheless, available statistical systems appear to be unable to hold long span dependencies and to model unfrequent structures. In fact, part of the weakness of statistical techniques may be compensated by rule-based methods. Furthermore, the application of rules during the probabilistic process inhibits the error propagation. Such an improvement could not be obtained by a post processing analysis. In order to take advantage of features that are complementary with two approaches, a hybrid approach has been followed in the design of an improved tagger called ECSta. In ECSta, as shown in this paper, a stack-decoding algorithm is combined with the Viterbi classical one.