Extracting Space Situational Awareness Events from News Text
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Abstract (arXiv)
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.
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References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019OpenNRE: An Open and Extensible Toolkit for Neural Relation Extractioncited by 62
- 2019Open Domain Event Extraction Using Neural Latent Variable Modelscited by 17
- 2021LightTag: Text Annotation Platformcited by 16
- 2020Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documentscited by 8
- 2019ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognitioncited by 6
- 2018Scalable Semantic Querying of Textcited by 1
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