Paper: A Metric-based Framework for Automatic Taxonomy Induction

ACL ID P09-1031
Title A Metric-based Framework for Automatic Taxonomy Induction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009

This paper presents a novel metric-based framework for the task of automatic taxonomy induction. The framework incrementally clus- ters terms based on ontology metric, a score indicating semantic distance; and transforms the task into a multi-criteria optimization based on minimization of taxonomy structures and modeling of term abstractness. It com- bines the strengths of both lexico-syntactic patterns and clustering through incorporating heterogeneous features. The flexible design of the framework allows a further study on which features are the best for the task under various conditions. The experiments not only show that our system achieves higher F1-measure than other state-of-the-art systems, but also re- veal the interaction between features and vari- ous types of rela...