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Hierarchical HMM-based semantic concept labeling model

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4 Author(s)
Kinfe Tadesse Mengistu ; Otto-von-Guericke University, Cognitive Systems Group, FEIT-IESK, 39106 Magdeburg, Germany ; Mirko Hannemann ; Tobias Baum ; Andreas Wendemuth

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.

Published in:

Spoken Language Technology Workshop, 2008. SLT 2008. IEEE

Date of Conference:

15-19 Dec. 2008