MICHAEL ELHADAD

We present an implemented procedure to select an appropriate connective to link two propositions, which is part of a large text generation system. Each connective is defined as a set of constraints between features of the propositions it connects. Our focus has been to identify pragmatic features that can be produced by a deep generator to provide a simple representation of connectives. Using these features, we can account for a variety of connective usages, and we can distinguish between similar connectives. We describe how a surface generator can produce complex sentences when given these features in input. The selection procedure is implemented as part of a large functional unification grammar.