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This paper presents a study on the use of deep syntactical features to improve prosody modeling. A French linguistic processing chain based on linguistic preprocessing, morpho-syntactical labeling, and deep syntactical parsing is used in order to extract syntactical features from an input text. These features are used to define more or less high-level syntactical feature sets. Such feature sets are compared on the basis of a HMM-based prosodic structure model. High-level syntactical features are shown to significantly improve the performance of the model (up to 21% error reduction combined with 19% BIC reduction).