tech,5-1-P01-1070,ak system </term> . We describe a set of <term> supervised machine learning experiments </term> centering on the construction of <term>
model,14-1-P01-1070,ak </term> centering on the construction of <term> statistical models </term> of <term> WH-questions </term> . These
other,17-1-P01-1070,ak of <term> statistical models </term> of <term> WH-questions </term> . These <term> models </term> , which
model,1-2-P01-1070,ak of <term> WH-questions </term> . These <term> models </term> , which are built from <term> shallow
other,7-2-P01-1070,ak models </term> , which are built from <term> shallow linguistic features </term> of questions , are employed to predict
other,22-2-P01-1070,ak target variables which represent a <term> user 's informational goals </term> . We report on different aspects
measure(ment),7-3-P01-1070,ak report on different aspects of the <term> predictive performance </term> of our <term> models </term> , including
model,11-3-P01-1070,ak predictive performance </term> of our <term> models </term> , including the influence of various
measure(ment),23-3-P01-1070,ak various training and testing factors on <term> predictive performance </term> , and examine the relationships among
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