Stochastic Representation of Conceptual Structure in the ATIS Task

Roberto Pieraccini, Esther Levin, Chin-Hui Lee

Abstract: We propose a model for a statistical representation of the conceptual structure in 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. The performance of the model was assessed on two tasks, including DARPA ATIS class A sentences.