Speech Communication - Special issue on interactive voice technology for telecommunication applications
An Integrated Statistical Model for Tagging and Chunking Unrestricted Text
TDS '00 Proceedings of the Third International Workshop on Text, Speech and Dialogue
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Tagging and chunking with bigrams
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Improving chunking by means of lexical-contextual information in statistical language models
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Evaluation of Prediction Methods Applied to an Inflected Language
TSD '02 Proceedings of the 5th International Conference on Text, Speech and Dialogue
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Over the last few years, stochastic models have been widely used in the natural language understanding modeling. Almost all of these works are based on the definition of segments of words as basic semantic units for the stochastic semantic models. In this work, we present a two-level stochastic model approach to the construction of the natural language understanding component of a dialog system in the domain of database queries. This approach will treat this problem in a way similar to the stochastic approach for the detection of syntactic structures (Shallow Parsing or Chunking) in natural language sentences; however, in this case, stochastic semantic language models are based on the detection of some semantic units from the user turns of the dialog. We give the results of the application of this approach to the construction of the understanding component of a dialog system, which answers queries about a railway timetable in Spanish.