A stochastic case frame approach for natural language understanding

A stochastically based approach for the semantic analysis component of a natural spoken language system for the ARPA Air Travel Information Services (ATIS) task has been developed. The semantic analyzer of the spoken language system already in use at LIMSI makes use of a rule-based case grammar. In this work, the system of rules for the semantic analysis is replaced with a relatively simple first-order hidden Markov model. The performances of the two approaches can be compared because they use identical semantic representations, despite their rather different methods for meaning extraction. We use an evaluation methodology that assesses performance at different semantic levels, including the database response comparison used in the ARPA ATIS paradigm