In this paper, we propose a language model based approach to classify user questions in the context of question answering systems. As categorization paradigm, a Bayes classifier is used to determine a corresponding semantic class. We present experiments with state-of-the-art smoothing methods as well as with some improved language models. Our results indicate that the techniques proposed here provide performance superior to the standard methods, including support vector machines.
Bibliographic reference. Merkel, Andreas / Klakow, Dietrich (2007): "Improved methods for language model based question classification", In INTERSPEECH-2007, 322-325.