Learning of domain dependent knowledge in semantic networks

For an e cient linguistic analysis of spoken queries a lot of domain speci c knowledge is needed and usually has to be entered manually into the knowledge base of each domain. This makes the adaption of dialogue systems which base on explicit knowledge representation to new domains a very costly procedure. We use a frequency based statistical method combined with general hidden markov models in order to learn domain speci c knowledge within a semantic network formalism. As a framework we use a dialogue system for German train timetable information. By means of experiments we show that our statistical approach is not only able to reach, but even outperforms previous results with manually entered restrictions.