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In this paper, we describe active and semi-supervised learning methods for reducing the labeling effort for spoken language understanding. In a goal-oriented call routing system, understanding the intent of the user can be framed as a classification problem. State of the art statistical classification systems are trained using a large number of human-labeled utterances, preparation of which is labor intensive and time consuming. Active learning aims to minimize the number of labeled utterances by automatically selecting the utterances that are likely to be most informative for labeling. The method for active learning we propose, inspired by certainty-based active learning, selects the examples that the classifier is the least confident about. The examples that are classified with higher confidence scores (hence not selected by active learning) are exploited using two semi-supervised learning methods. The first method …