The extraction of temporal events from text and the classification of temporal relations among both temporal events and time expressions are major challenges for the interface of data mining and natural language processing. We present an ensemble method, which reconciles the outputs of multiple heterogenous classifiers of temporal expressions. We use integer programming, a constrained optimisation technique, to improve on the best result of any individual classifier by choosing consistent temporal relations from among those recommended by multiple classifiers. Our ensemble method is conceptually simple and empirically powerful. It allows us to encode knowledge about the structure of valid temporal expressions as a set of constraints. It obtains new state-of-the-art results on two recent natural language processing challenges, SemEval-2013 TempEval-3 (Temporal Annotation) and SemEval-2016 Task 12 (Clinical TempEval), with F1 scores of 0.3915 and 0.595 respectively.

中文翻译：

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时间关系分类器的整数编程合奏

从文本中提取时间事件以及对时间事件和时间表达之间的时间关系进行分类是数据挖掘和自然语言处理接口的主要挑战。我们提出了一种集成方法，该方法调和了时间表达的多个异类分类器的输出。我们使用整数规划（一种有约束的优化技术），通过从多个分类器推荐的时间关系中选择一致的时间关系来改善任何单个分类器的最佳结果。我们的集成方法在概念上很简单，在经验上也很强大。它使我们可以将关于有效时间表达式的结构的知识编码为一组约束。它针对两个最近的自然语言处理挑战获得了最新的技术成果，