Statistical natural language understanding using hidden clumpings

Abstract

We present a new approach to natural language understanding (NLU) based on the source-channel paradigm, and apply it to ARPA's Air Travel Information Service (ATIS) domain. The model uses techniques similar to those used by IBM in statistical machine translation. The parameters are trained using the EM algorithm; a hierarchy of models is used to facilitate the bootstrapping of more complex models from simpler models.