Previous studies in data-driven dependency parsing have shown that tree transformations can improve parsing accuracy for specific parsers and data sets. We investigate to what ex...
Graphs are powerful data structures that have many attractive properties for object representation. However, some basic operations are difficult to define and implement, for ins...
Miquel Ferrer, Ernest Valveny, Francesc Serratosa,...
An algorithm for learning structural patterns given in terms of Attributed Relational Graphs (ARG's) is presented. The algorithm, based on inductive learning methodologies, pr...
This paper introduces a general framework for defining the entropy of a graph. Our definition is based on a local information graph and on information functionals derived from the...
Abstract. In this paper we explore a topic which is at the intersection of two areas of Machine Learning: namely Support Vector Machines (SVMs) and Inductive Logic Programming (ILP...
Stephen Muggleton, Huma Lodhi, Ata Amini, Michael ...