The Message Transformation Model (MTM), for modeling complex message transformation processes in data centric application scenarios, provides strong capabilities for describing the...
String transformation systems have been introduced in (Brill, 1995) and have several applications in natural language processing. In this work we consider the computational proble...
Sparse Orthonormal Transforms (SOT) has recently been proposed as a data compression method that can achieve sparser representations in transform domain. Given initial conditions,...
Weintroduce a significant improvementfor a relatively newmachine learning methodcalled Transformation-Based Learning. By applying a MonteCarlo strategy to randomly sample from the...
As social networks are becoming ubiquitous on the Web, the Semantic Web goals indicate that it is critical to have a standard model allowing exchange, interoperability, transformat...