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Locating documents carrying positive or negative favourability is an important application within media analysis. This article presents some empirical results on the challenges facing a machine-learning approach to this kind of opinion mining. Some of the challenges include: the often considerable imbalance in the distribution of positive and negative(More)
This paper describes the underlying principles of a computer model, CHREST+, which learns to solve problems using diagrammatic representations. Although earlier work has determined that experts store domain-specific information within schemata, no substantive model has been proposed for learning such representations. We describe the different strategies(More)
This paper argues that the CHREST architecture of cognition can shed important light on developing artificial general intelligence. The key theme is that " cognition is perception. " The description of the main components and mechanisms of the architecture is followed by a discussion of several domains where CHREST has already been successfully applied,(More)
Computational models of learning provide an alternative technique for identifying the number and type of chunks used by a subject in a specific task. Results from applying CHREST to chess expertise support the theoretical framework of Cowan and a limit in visual short-term memory capacity of 3-4 items. An application to learning from diagrams illustrates(More)
Quantitative predictions for complex scientific theories are often obtained by running simulations on computational models. In order for a theory to meet with widespread acceptance, it is important that the model be reproducible and comprehensible by independent researchers. However, the complexity of computational models can make the task of replication(More)
(Received 00 Month 200x; In final form 00 Month 200x) Computer implementations of theoretical concepts play an ever-increasing role in the development and application of scientific ideas. As the scale of such implementations increases from relatively small models and empirical setups to overarching frameworks from which many kinds of results may be(More)