Natural language parsing as statistical pattern recognition
Natural language parsing as statistical pattern recognition
Modeling spoken dialog systems under the interactive pattern recognition framework
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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This paper presents a comparison of a rule-based and a statistical semantic information modeling technique. For the rule--based method we employ Embedded Grammar (EG) tagging and for the statistical method we use a previously proposed Semantic Structured Language Modeling (SSLM) technique. Both EG and SSLM achieve around 15% relative improvement in speech recognition performance over the baseline dialog state--based trigram language model in a financial transaction domain. Combining EG and SSLM using linear interpolation results in further improvement. We also use the features obtained from EG and SSLM for confidence measurement. Word level confidence measurement experiments using EG and SSLM--based semantic features combined with posterior probability show over 20% relative improvement in correct acceptance rate (CA) at 5% false alarm (FA) rate over the posterior probability based feature. In both language model rescoring and confidence measurement experiments SSLM outperforms EG by a small margin.