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An utterance can be conceived as a hidden sequence of semantic concepts expressed in words or phrases. The problem of understanding the meaning underlying a spoken utterance in a dialog system can be partly solved by decoding the hidden sequence of semantic concepts from the observed sequence of words. In this paper, we describe a hierarchical HMM-based semantic concept labeling model trained on semantically unlabeled data. The hierarchical model is compared with a flat concept based model in terms of performance, ambiguity resolution ability and expressive power of the output. It is shown that the proposed method outperforms the flat-concept model in these points.