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Shrinkage based features for slot tagging with conditional random fields

In this paper we propose a set of class-based features that are generated in an unsupservised fashion to improve slot tagging with Conditional Random Fields (CRFs). The feature generation is based on the idea behind shrinkage based language models, where shrinking the sum of parameter magnitudes in an exponential model tends to improve performance. We use these features with CRFs and show that they consistently improve the slot tagging performance against baselines on several natural language understanding tasks. Since the proposed features are generated in an unsupervised manner without significant computational overhead, the improvements in performance comes for free and we expect that the same features may result in gains in other tagging tasks.