Taxonomy Extraction from Automotive Natural Language Requirements Using Unsupervised Learning

Martin Ringsquand, Mathias Schraps
2014 International Journal on Natural Language Computing  
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
more » ... reedupon 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.
doi:10.5121/ijnlc.2014.3403 fatcat:aukdfc5ibbbatgwxtvn4bldgeu