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.

• Type: Conference
• Year: 2001
• Where: ACL
• Authors: Ingrid Zuckerman, Eric Horvitz
• Keywords: ACL 2001, ACL 2007, Predictive Performance, Shallow Linguistic Features, Target Variables