Distributional Term Set Expansion
This is an incremental study improving term set expansion for users in natural language processing by demonstrating the superiority of active learning approaches.
The paper compared centrality-based and active learning classification methods for iterative term set expansion in distributional semantic models, finding that active learning methods consistently outperformed centrality-based methods across five term sets.
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.