Abstract
We propose a model for a statistical representation of the conceptual structure of a restricted subset of spoken natural language. The model is used for segmenting a sentence into phrases and labeling them with concept relations (or cases). The model is trained using a corpus of annotated transcribed sentences. An understanding system is being built around this model, allowing for unconstrained spoken input in a database retrieval task. The scope of this paper is to give details and results concerning the new language representation model. To that aim, the model was implemented and tested allowing a text input. While the model parameters were estimated using 547 training sentences, the results on a test set of 148 sentences showed that almost 97% of the concepts were correctly detected and labeled by the automatic concept labeling procedure; eventually, 65% of the sentences were correctly understood.
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