Methods and Models in Linguistic and Musical Computational Creativity

Computational creativity is an area of artificial intelligence that develops algorithms and simulations of creative phenomena, as well as tools for performing creative tasks. In this thesis, we present various computational methods and models of linguistic and musical creativity. The emphasis is on developing methods that are maximally unsupervised, i.e. methods that require a minimal amount of hand-crafted linguistic, world, or domain knowledge. This thesis consists of an introductory part and five original research articles. The introductory part outlines computational creativity as a research field and discusses some of the philosophical foundations underlying the current work. The research articles present specific methods and algorithms for automatic composition of poetry and songs. The first article proposes a corpus-based poetry generation method that relies on statistical language modelling and morphological analysis and synthesis. In the second article, we expand that basic model with constraint programming techniques to handle more aspects of the poetic structure and style. The third article presents a method for mining document-specific word associations and proposes using them in poetry generation to produce poems based, for instance, on a specific news story. The fourth article presents a song composition system that utilises constraint programming to produce songs with matching lyrics and music in a transformational way, i.e. is able to modify its