We show that any weak ranker that can achieve an area under the ROC curve slightly better than 1/2 (which can be achieved by random guessing) can be effi- ciently boosted to achieve an area under the ROC curve arbitrarily close to 1. We further show that this boosting can be performed even in the presence of indepen- dent misclassification noise, given access to a noise-tolerant weak ranker.

Automated sports commentary is a form of automated narrative. Sports commentary exists to keep the viewer informed and entertained. One way to entertain the viewer is by telling brief stories relevant to the game in progress. We introduce a system called the Sports Commentary Recommendation System (SCoReS) that can automatically suggest stories for commentators to tell during games. Through several user studies, we compared commentary using SCoReS to three other types of commentary and show that SCoReS adds significantly to the broadcast across several enjoyment metrics. We also collected interview data from professional sports commentators who positively evaluated a demonstration of the system. We conclude that SCoReS can be a useful broadcast tool, effective at selecting stories that add to the enjoyment and watchability of sports. SCoReS is a step toward automating sports commentary and, thus, automating narrative.

We show that any weak ranker that can achieve an area under the ROC curve slightly better than 1/2 (which can be achieved by random guessing) can be efficiently boostedto achieve an area under the ROC curve arbitrarily close to 1. We further show that this boosting can be performed even in the presence of independent misclassificationnoise, given access to a noise-tolerant weak ranker.

We show that any weak ranker that can achieve an area under the ROC curve slightly better than 1/2 (which can be achieved by random guessing) can be efficiently boosted to achieve an area under the ROC curve arbitrarily close to 1. We further show that this boosting can be performed even in the presence of independent misclassification noise, given access to a noise-tolerant weak ranker.

We show that any weak ranker that can achieve an area under the ROC curve slightly better than 1/2 (which can be achieved by random guessing) can be efficiently boosted to achieve an area under the ROC curve arbitrarily close to 1. We further show that this boosting can be performed even in the presence of independent misclassification noise, given access to a noise-tolerant weak ranker.