Ingrid Zukerman and Eric Horvitz,
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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.
Keywords: Question answering, machine learning, natural language processing, WH-queries, informational goals, level of detail.
In: Proceedings of the Conference of the Association for Computational Linguistics, ACL 2001, Toulouse, France, July 2001.
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