Abstract. When creating execution-level process models from conceptual to-be process models, challenges are to find implementations for process activities and to use these impleme...
In this paper we propose a random set framework for learning linguistic models for prediction problems. We show how we can model prediction problems based on learning linguistic p...
Computational models of meaning trained on naturally occurring text successfully model human performance on tasks involving simple similarity measures, but they characterize meani...
Marco Baroni, Brian Murphy, Eduard Barbu, Massimo ...
Abstract— We present and evaluate a novel constraintbased model transformation approach that implements a preservation-centric view. The proposed framework comprises formal prese...
This paper presents a computational self-organizing model of multi-modal information, inspired from cortical maps. It shows how the organization in a map can be influenced by the ...