A Metric-based Framework for Automatic Taxonomy Induction

This paper presents a novel metric-based framework for the task of automatic taxonomy induction. The framework incrementally clusters 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 combines 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 reveal the interaction between features and various types of relations, as well as the interacween features and term abstractness.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Hui Yang, Jamie Callan
• Keywords: ACL 2009, Automatic Taxonomy Induction, Computational Linguistics, Multi-criteria Optimization, Term Abstractness