Extracting Space Situational Awareness Events from News Text
This work addresses the need for automated event extraction in space situational awareness, which is incremental as it applies existing methods to a new textual data source.
The paper tackled the problem of extracting space situational awareness events from news text, achieving an overall F1 score between 53 and 91 per slot for event extraction in this low-resource domain.
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.