Using Machine Learning Techniques to Interpret WH-questions 
 We describe a set of supervised machine learning experiments centering on the construction of statistical models of WH-questions . These models , which are built from shallow linguistic features of questions , are employed to predict target variables which represent a user's informational goals . We report on different aspects of the predictive performance of our models , including the influence of various training and testing factors on predictive performance , and examine the relationships among the target variables. 