Enhancing machine learning results for semantic relation extraction

Abstract

This paper describes a large scale method to extract semantic relations between named entities. It is characterized by a large number of relations and can be applied to various domains and languages. Our approach is based on rule mining from an Arabic corpus using lexical, semantic and numerical features. Three primordial steps are needed: Firstly, we extract the learning features from annotated examples. Then, a set of rules are generated automatically using three learning algorithms which are Apriori, Tertius and the decision tree algorithm C4.5. Finally, we add a module of significant rules selection in which we use an automatic technique based on many experiments. We achieved satisfactory results when applied to our test corpus. © 2013 Springer-Verlag Berlin Heidelberg.