Modelling Creativity: Identifying Key Components through a Corpus-Based Approach

Abstract

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research.

Fig 1. A flow diagram describing the search strategy used to identify papers for the creativity corpus.

Fig 2. Representation of the disciplinary breakdown of the Creativity Corpus by time period.

Disciplines are as specified for the paper’s journal, by the academic database Scopus. Note that Scopus may classify a journal under more than one discipline.

Fig 3. A flow diagram describing the search strategy used to identify papers for the non-creativity corpus.

Fig 4. Word pairwise similarity data visualised as an edge-weighted graph.

Nodes correspond to words and edges are weighted by similarity scores (for any score > 0).

Fig 5. Sample of clusters produced by the Chinese Whispers clustering step.

Fig 6. Illustration of the process of using manual inspection for further clustering.

Fig 7. The fourteen key components of creativity identified through an analysis of the word clusters.

Fig 8. The ontology of creativity generated from this work’s results, in graph form.