  The extraction and understanding of temporal events and their relations are
major challenges in natural language processing. Processing text on a
sentence-by-sentence or expression-by-expression basis often fails, in part due
to the challenge of capturing the global consistency of the text. We present an
ensemble method, which reconciles the outputs of multiple classifiers of
temporal expressions across the text using integer programming. Computational
experiments show that the ensemble improves upon the best individual results
from two recent challenges, SemEval-2013 TempEval-3 (Temporal Annotation) and
SemEval-2016 Task 12 (Clinical TempEval).
