To facilitate the development of speech enabled applications and services, we have been working on an example-based semantic grammar authoring tool. Previous studies have shown that the tool has not only significantly reduced the grammar development effort but also yielded grammars of better qualities. However, the tool requires extra human involvement when ambiguities exist in the process of grammar rule induction. In this paper we present an algorithm that is able to automatically resolve the segmentation ambiguities, hence acquire the language expressions for the concepts involved. Preliminary experiment results show that the expectation-maximization algorithm we investigated has not only eliminated the human involvement in ambiguity resolution but also improved the overall spoken language understanding accuracy.