We report on different aspects of the
<term>
predictive performance
</term>
of our
<term>
models
</term>
, including the influence of various training and testing factors on
<term>
predictive performance
</term>
, and examine the relationships among the target variables .
#2182We 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
These
<term>
models
</term>
, which are built from
<term>
shallow linguistic features
</term>
of questions , are employed to predict target variables which represent a
<term>
user 's informational goals
</term>
.
#2145Thesemodels, which are built from shallow linguistic features of questions, are employed to predict target variables which represent a user's informational goals.
measure(ment),23-3-P01-1070,ak
We report on different aspects of the
<term>
predictive performance
</term>
of our
<term>
models
</term>
, including the influence of various training and testing factors on
<term>
predictive performance
</term>
, and examine the relationships among the target variables .
#2194We 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.
measure(ment),7-3-P01-1070,ak
We report on different aspects of the
<term>
predictive performance
</term>
of our
<term>
models
</term>
, including the influence of various training and testing factors on
<term>
predictive performance
</term>
, and examine the relationships among the target variables .
#2178We 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
These
<term>
models
</term>
, which are built from
<term>
shallow linguistic features
</term>
of questions , are employed to predict target variables which represent a
<term>
user 's informational goals
</term>
.
#2151These 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
We describe a set of
<term>
supervised machine learning experiments
</term>
centering on the construction of
<term>
statistical models
</term>
of
<term>
WH-questions
</term>
.
#2139We describe a set of supervised machine learning experiments centering on the construction ofstatistical models of WH-questions.
tech,5-1-P01-1070,ak
We describe a set of
<term>
supervised machine learning experiments
</term>
centering on the construction of
<term>
statistical models
</term>
of
<term>
WH-questions
</term>
.
#2130We describe a set ofsupervised machine learning experiments centering on the construction of statistical models of WH-questions.
other,22-2-P01-1070,ak
These
<term>
models
</term>
, which are built from
<term>
shallow linguistic features
</term>
of questions , are employed to predict target variables which represent a
<term>
user 's informational goals
</term>
.
#2166These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent auser 's informational goals.
other,17-1-P01-1070,ak
We describe a set of
<term>
supervised machine learning experiments
</term>
centering on the construction of
<term>
statistical models
</term>
of
<term>
WH-questions
</term>
.
#2142We describe a set of supervised machine learning experiments centering on the construction of statistical models ofWH-questions.