This paper introduces a new method for identifying named-entity (NE) transliterations within bilingual corpora. Current state-of-theart approaches usually require annotated data and relevant linguistic knowledge which may not be available for all languages. We show how to effectively train an accurate transliteration classifier using very little data, obtained automatically. To perform this task, we introduce a new active sampling paradigm for guiding and adapting the sample selection process. We also investigate how to improve the classifier by identifying repeated patterns in the training data. We evaluated our approach using English, Russian and Hebrew corpora.