This article presents a series of experiments in automatically generating poetic texts. We confined our attention to the generation of texts which are syntactically well-formed, meet certain pre-specified patterns of metre and broadly convey some given meaning. Such aspects can be formally defined, thus avoiding the complications of imagery and interpretation that are central to assessing more free forms of verse. Our implemented system, McGONAGALL, applies the genetic algorithm to construct such texts. It uses a sophisticated linguistic formalism to represent its genomic information, from which can be computed the phenotypic information of both semantic representations and patterns of stress. The conducted experiments broadly indicated that relatively meaningful text could be produced if the constraints on metre were relaxed, and precise metric text was possible with loose semantic constraints, but it was difficult to produce text which was both semantically coherent and of high quality metrically.

This paper presents a technique for sentence generation.We argue that the input to generators should have a non-hierarchical nature.This allows us to investigate a more general version of the sentence generation problem where one is not pre-committed to a choice of the syntactically prominent elements in the initial semantics.We also consider that a generator can happen to convey more (or less) information than is originally specified in its semantic input.In order to constrain this approximate matching of the input we impose additional restrictions on the semantics of the generated sentence.Our technique provides flexibility to address cases where the entire input cannot be precisely expressed in a single sentence.Thus the generator does not rely on the strategic component having linguistic knowledge.We show clearly how the semantic structure is declaratively related to linguistically motivated syntactic representation.