A Supervised Machine Learning Approach for Taxonomic Relation Recognition through Non-linear Enumerative Structures

Abstract

Improving relation extraction process requires to have a better insight of the proper text or to use external resources. Our work lies in the first term of this alternative, and aim at extending works about semantic relation identification in texts for building taxonomies which constitute the backbone of ontologies on which Semantic Web applications are built. We consider a specific discursive structure, the enumerative structure, as it bears explicit hierarchical knowledge. This structure is expressed with the help of lexical or typo-dispositional markers whose role is to introduce hierarchical levels between its components. Typo-dispositional markers are unfortunately not integrated into most parsing systems used for information extraction tasks. In order to extend the taxonomic relation identification process, we thus propose a method for recognizing this relation through enumerative structures which benefit from typo-dispositional markers (we called them non-linear enumerative structures). Our method is based on supervised machine learning. Two strategies have been applied: a linear classification with a MaxEnt and a non-linear one with a SVM. The results obtained in each of these approaches are close, with respectively an F1 of 81.25% and of 81.77%.