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Enhancing Machine Learning Results for Semantic Relation Extraction

  • Ines Boujelben
  • Salma Jamoussi
  • Abdelmajid Ben Hamadou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7934)

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.

Keywords

Semantic relation Named Entity supervised learning rules mining rules selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ines Boujelben
    • 1
  • Salma Jamoussi
    • 1
  • Abdelmajid Ben Hamadou
    • 1
  1. 1.Technopole of SfaxMiracl-Sfax UniversitySfaxTunisia

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