Understanding with Question Answering

QA TempEval shifts the goal of previous TempEvals away from an intrinsic evaluation methodology toward a more extrinsic goal of question answering. This evaluation requires systems to capture temporal information rele-vant to perform an end-user task, as opposed to corpus-based evaluation where all temporal information is equally important. Evaluation results show that the best automated TimeML annotations reach over 30 % recall on ques-tions with ‘yes ’ answer and about 50 % on eas-ier questions with ‘no ’ answers. Features that helped achieve better results are event corefer-ence and a time expression reasoner.