| measure(ment),23-3-P01-1070,ak | various training and testing factors on <term> | predictive performance | </term> , and examine the relationships among | #2194 We report on different aspects of the predictive performance of our models, including the influence of various training and testing factors onpredictive performance, and examine the relationships among the target variables. | |
| model,11-3-P01-1070,ak | predictive performance </term> of our <term> | models | </term> , including the influence of various | #2182 We report on different aspects of the predictive performance of ourmodels, including the influence of various training and testing factors on predictive performance, and examine the relationships among the target variables. | |
| model,1-2-P01-1070,ak | of <term> WH-questions </term> . These <term> | models | </term> , which are built from <term> shallow | #2145 Thesemodels, which are built from shallow linguistic features of questions, are employed to predict target variables which represent a user's informational goals. | |
| other,17-1-P01-1070,ak | of <term> statistical models </term> of <term> | WH-questions | </term> . These <term> models </term> , which | #2142 We describe a set of supervised machine learning experiments centering on the construction of statistical models ofWH-questions. | |
| other,22-2-P01-1070,ak | target variables which represent a <term> | user 's informational goals | </term> . We report on different aspects | #2166 These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent auser 's informational goals. | |
| 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> | #2130 We describe a set ofsupervised machine learning experiments centering on the construction of statistical models of WH-questions. | |
| measure(ment),7-3-P01-1070,ak | report on different aspects of the <term> | predictive performance | </term> of our <term> models </term> , including | #2178 We report on different aspects of thepredictive performance of our models, including the influence of various training and testing factors on predictive performance, and examine the relationships among the target variables. | |
| other,7-2-P01-1070,ak | models </term> , which are built from <term> | shallow linguistic features | </term> of questions , are employed to predict | #2151 These models, which are built fromshallow linguistic features of questions, are employed to predict target variables which represent a user's informational goals. | |
| model,14-1-P01-1070,ak | </term> centering on the construction of <term> | statistical models | </term> of <term> WH-questions </term> . These | #2139 We describe a set of supervised machine learning experiments centering on the construction ofstatistical models of WH-questions. |
