This article presents a two-step methodology to annotate temporally anchored spatial knowledge on top of OntoNotes. We first generate potential knowledge using semantic roles or syntactic dependencies and then crowdsource annotations to validate the potential knowledge. The resulting annotations indicate how long entities are or are not located somewhere and temporally anchor this spatial information. We present an in-depth corpus analysis comparing the spatial knowledge generated by manipulating roles or dependencies. Experiments show that working with syntactic dependencies instead of semantic roles allows us to generate more potential entity-related spatial knowledge and obtain better results in a realistic scenario, that is, with predicted linguistic information.

中文翻译：

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时间锚定的空间知识：语料库和实验

本文介绍了一种在 OntoNotes 之上注释时间锚定的空间知识的两步方法。我们首先使用语义角色或句法依赖生成潜在知识，然后众包注释以验证潜在知识。生成的注释指示实体位于或不位于某处的时间长短，并在时间上锚定此空间信息。我们提出了一个深入的语料库分析，比较了通过操纵角色或依赖关系产生的空间知识。实验表明，使用句法依赖而不是语义角色可以让我们生成更多潜在的与实体相关的空间知识，并在现实场景中获得更好的结果，即使用预测的语言信息。