kLogNLP: Graph Kernel-based Relational Learning of Natural Language

Mathias Verbeke, Paolo Frasconi, Kurt De Grave, Fabrizio Costa, Luc De Raedt

Abstract: kLog is a framework for kernel-based learning that has already proven successful in solving a number of relational tasks in natural language processing. In this paper, we present kLogNLP, a natural language processing module for kLog. This module enriches kLog with NLP-specific preprocessors, enabling the use of existing libraries and toolkits within an elegant and powerful declarative machine learning framework. The resulting relational model of the domain can be extended by specifying additional relational features in a declarative way using a logic programming language. This declarative approach offers a flexible way of experimentation and a way to insert domain knowledge.