The understanding module of a spoken dialogue system must extract, from the speech recognizer output, the kind of request expressed by the caller (the call type) and its parameters (numerical expressions, time expressions or proper-name). The definition of such parameters (called Named Entities, NE) is linked to the dialogue application. Detecting and extracting such contextual NEs for the How May I Help You? application is the subject of this study. By detecting NEs with a statistical tagger on 1-best hypotheses and by extracting their values with local models on word-lattices, we show very significant improvements compared to the traditional approach which uses regular expressions on the 1-best hypothesis only.