Paper: Using Machine Learning Techniques To Interpret WH-Questions

ACL ID P01-1070
Title Using Machine Learning Techniques To Interpret WH-Questions
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2001
Authors

We describe a set of supervised ma- chine learning experiments centering on the construction of statistical mod- els of WH-questions. These models, which are built from shallow linguis- tic features of questions, are employed to predict target variables which repre- sent a user’s informational goals. We report on different aspects of the pre- dictive performance of our models, in- cluding the influence of various training and testing factors on predictive perfor- mance, and examine the relationships among the target variables.