Combining statistical and syntactical systems for spoken language understanding with graphical models

There are two basic approaches for semantic processing in spoken language understanding: a rule based approach and a statistic approach. In this paper we combine both of them in a novel way by using statistical and syntactical dynamic bayesian networks (DBNs) together with Graphical Models (GMs) for spoken language understanding (SLU). GMs merge in a complex, mathematical way probability with graph theory. This results in four different setups which raise in their complexity. Comparing our results to a baseline system we achieve a F1-measure of 93:7% in word classes and 95:7% in concepts for our best setup in the ATIS-Task. This outperforms the baseline system relatively by 3:7% in word classes and by 8:2% in concepts. The expermiments were performend with the graphical model toolkit (GMTK).