Approach to Spoken Chinese Understanding Based on Semantic Classification Trees

The spoken language understanding is a crucial part in spoken language translation systems and human-machine dialog systems.In this paper,we propose a new approach to spoken Chinese understanding which combines statistical and rule-based methods.In this approach,the semantic classification trees which are built by the semantic rules automatically learned from the training data are used to disambiguate key words related to the sentences'shallow semantic meaning,and then,a statistical model is used to extract the whole sentence's domain action.The experimental results show that this approach has good performance and is feasible for the restricted domain oriented Chinese spoken language understanding in the shallow semantic level.