论文标题
从新闻文本中提取太空情境意识事件
Extracting Space Situational Awareness Events from News Text
论文作者
论文摘要
空间情境意识通常利用雷达，望远镜和其他资产的物理测量，以监视卫星和其他航天器，以进行操作，导航和防御目的。在这项工作中，我们使用文本输入来探索空间情境意识任务。我们在2009年至2020年间构建了涵盖所有已知活跃卫星的48.5k新闻文章。使用基于依赖性规则的提取系统，旨在针对三个高影响事件（航天器发射，失败和退役），我们确定1,787个太空事件句子，然后由人类与15.9k Labels for Event Slots相关。我们从经验上证明，在这个低资源，高影响力域中，每插槽的总体F1在每个插槽中达到53至91之间的总体F1。
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.
