Space situational awareness typically makes use of physical measurements from radar, telescopes, and other assets to monitor satellites and other spacecraft for operational, navigational, and defense purposes. In this work we explore using textual input for the space situational awareness task. We construct a corpus of 48.5k news articles spanning all known active satellites between 2009 and 2020. Using a dependency-rule-based extraction system designed to target three high-impact events -- spacecraft launches, failures, and decommissionings, we identify 1,787 space-event sentences that are then annotated by humans with 15.9k labels for event slots. We empirically demonstrate a state-of-the-art neural extraction system achieves an overall F1 between 53 and 91 per slot for event extraction in this low-resource, high-impact domain.
翻译:空间形势认识通常利用雷达、望远镜和其他资产进行物理测量,监测卫星和其他航天器的运行、导航和防御用途。在这项工作中,我们探索利用文字投入开展空间形势认识工作。我们制作了48.5k条新闻文章,涵盖2009年至2020年期间所有已知的有效卫星。我们使用依赖规则的提取系统,针对三种影响大的事件 -- -- 航天器发射、故障和退役 -- -- 确定1 787个空间活动句子,然后由人类用15.9k的标签对事件位置进行附加说明。我们从经验上证明,最先进的神经提取系统在资源低、影响大的领域每个事件提取时间段达到53至91个总F1。