  This paper is a short empirical study of the performance of centrality and
classification based iterative term set expansion methods for distributional
semantic models. Iterative term set expansion is an interactive process using
distributional semantics models where a user labels terms as belonging to some
sought after term set, and a system uses this labeling to supply the user with
new, candidate, terms to label, trying to maximize the number of positive
examples found. While centrality based methods have a long history in term set
expansion, we compare them to classification methods based on the the Simple
Margin method, an Active Learning approach to classification using Support
Vector Machines. Examining the performance of various centrality and
classification based methods for a variety of distributional models over five
different term sets, we can show that active learning based methods
consistently outperform centrality based methods.
