Hidden Markov model-based Supertagging in a user-initiative dialogue system
Jens Bäcker, Karin Harbusch · 2002
In this paper we outline the advantages of deploying a shallow parser based on Supertagging in an automatic dialogue system in a call center that basically leaves the initiative with the user as far as (s)he wants (in the literature called user-- initiative or adaptive in contrast to system--initiative dialogue systems). The Supertagger relies on a Hidden Markov model and is trained with German input texts. The entire design of a Hidden Markov--based Supertagger with trigrams builds the central issue of this paper. The evaluation of our German Supertagger lags behind the English one. Some of the reasons will be addressed later on. Nevertheless shallow parsing with the Supertags increases the accuracy compared to a basic version of KoHDaS that only relies on recurrent plausibility networks. 1.