Taxonomy Extraction from Automotive Natural Language Requirements Using Unsupervised Learning
Martin Ringsquand, Mathias Schraps · International Journal on Natural Language Computing · 2014
In this paper we present a novel approach to semi-automatically learn concept hierarchies from natural language requirements of the automotive industry.The approach is based on the distributional hypothesis and the special characteristics of domain-specific German compounds.We extract taxonomies by using clustering techniques in combination with general thesauri.Such a taxonomy can be used to support requirements engineering in early stages by providing a common system understanding and an agreedupon terminology.This work is part of an ontology-driven requirements engineering process, which builds on top of the taxonomy.Evaluation shows that this taxonomy extraction approach outperforms common hierarchical clustering techniques.