This paper proposes two principled methods to incorporate semantic information into word level confidence measurement. The first technique uses tag and arc probabilities obtained from a statistical classer and parser tree. The second technique uses a maximum entropy based semantic structured language model to use semantic structure of a sentence to assign semantic probabilities to each word. Semantic features provide significant improvements over a posterior probability based confidence measure when used together in an air travel reservation task.
['Ruhi Sarikaya', 'Yuqing Gao', 'Michael Picheny']
Word level confidence measurement using semantic features