# Extracting Space Situational Awareness Events from News Text

arXiv: https://arxiv.org/abs/2201.05721 · HTML: https://mlsanity.com/paper/2201.05721/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 15 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction](https://mlsanity.com/paper/1909.13078/) (2019)
- [Open Domain Event Extraction Using Neural Latent Variable Models](https://mlsanity.com/paper/1906.06947/) (2019)
- [LightTag: Text Annotation Platform](https://mlsanity.com/paper/2109.02320/) (2021)
- [Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents](https://mlsanity.com/paper/2002.01861/) (2020)
- [ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition](https://mlsanity.com/paper/1911.10436/) (2019)
- [Scalable Semantic Querying of Text](https://mlsanity.com/paper/1805.01083/) (2018)

## 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.