An information theoretic approach for using word cluster information in natural language call routing

In this paper, an information theoretic approach for using word clusters in natural language call routing (NLCR) is proposed. This approach utilizes an automatic word class clustering algorithm to generate word classes from the word based training corpus. In our approach, the information gain (IG) based term selection is used to combine both word term and word class information in NLCR. A joint latent semantic indexing natural language understanding algorithm is derived and studied in NLCR tasks. Comparing with word term based approach, an average performance gain of 10.7% to 14.5% is observed averaged over various training and testing conditions.