Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.

Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this paper, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, i.e. language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.

We describe a set of experiments using automatically labelled data to train supervised classifiers for multi-class emotion detection in Twitter messages with no manual intervention. By cross-validating between models trained on different labellings for the same six basic emotion classes, and testing on manually labelled data, we conclude that the method is suitable for some emotions (happiness, sadness and anger) but less able to distinguish others; and that different labelling conventions are more suitable for some emotions than others.

Executive Summary In this review, we bring together conceptual representations relevant to concept creation in the scope of ConCreTe. The conceptual representations reviewed are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial, and connectionist representations. The other is between descriptive and procedural representations. These two distinctions are orthogonal. Moreover, conceptual representations used in particular creative domains, i.e., language, music, image, and emotion, are reviewed separately. For each representation reviewed, we also cover the inference it affords, the computational means of building it, and its application in concept creation. In the end, we propose a high-level categorization of concept formation, and indicate directions of future research, as identified during this review, according to the proposed categories. Dissemination Level PU Public X PP Restricted to other programme participants (including the Commission Services)-RE Restricted to a group specified by the Consortium (including the Commission Services)-CO Confidential, only for members of the Consortium (including the Commission Services)-The ConCreTe Consortium has addressed all comments received, making changes as necessary. Changes to this document are detailed in the change log table below.