Simulating Morphological Analyzers with Stochastic Taggers for Confidence Estimation

We propose a method for providing stochastic confidence estimates for rule-based and black-box natural language (NL) processing systems. Our method does not require labeled training data: We simply train stochastic models on the output of the original NL systems. Numeric confidence estimates enable both minimum Bayes risk

• Type: Conference
• Year: 2009
• Where: CLEF
• Authors: Christian Monson, Kristy Hollingshead, Brian Roark
• Keywords: CLEF 2009, Confidence Estimates, Information Technology, Numeric Confidences, Unsupervised Morphology Induction