Language provides speakers with a rich system of modality for expressing thoughts about events, without being committed to their actual occurrence. Modality is commonly used in the political news domain, where both actual and possible courses of events are discussed. NLP systems struggle with these semantic phenomena, often incorrectly extracting events which did not happen, which can lead to issues in downstream applications. We present an open-domain, lexicon-based event extraction system that captures various types of modality. This information is valuable for Question Answering, Knowledge Graph construction and Fact-checking tasks, and our evaluation shows that the system is sufficiently strong to be used in downstream applications.

翻译：语言为发言者提供了一个表达对事件的想法的丰富模式系统,而不必致力于实际发生,政治新闻领域通常使用模式,讨论实际和可能的事件课程。国家语言方案系统与这些语义现象作斗争,往往错误地提取没有发生的事件,这可能导致下游应用的问题。我们提出了一个基于词汇的开放事件提取系统,记录了各种类型的模式。这一信息对于问答、知识图构建和实况调查任务很有价值,我们的评估表明,该系统足够强大,可用于下游应用。