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 prohabilities to each word. Semantic features provide significant improvements over a posterior probability based confidence measure when used together in an air travel reservation task.
REF explored two sets of semantic features: one set from a statistical classer/parser, and the other set from a maximum entropy based semantic-structured language model.
Word level confidence measurement using semantic features
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).
Computer Science