Citation
Xie, Z., Kwak, A. S., George, E., Dozal, L. W., Van, H., Jah, M., ... & Jansen, P. (2022). Extracting Space Situational Awareness Events from News Text. arXiv preprint arXiv:2201.05721.Rights
© 2022. The Author(s). This work uses a Creative Commons CC BY license: https://creativecommons.org/licenses/by/4.0/.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
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. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.Note
Open access journalISBN
9791095546726Version
Final published versionae974a485f413a2113503eed53cd6c53
10.48550/arXiv.2201.05721
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Except where otherwise noted, this item's license is described as © 2022. The Author(s). This work uses a Creative Commons CC BY license: https://creativecommons.org/licenses/by/4.0/.