# ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19

arXiv: https://arxiv.org/abs/2105.01819 · HTML: https://mlsanity.com/paper/2105.01819/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [Deep Structured Neural Network for Event Temporal Relation Extraction](https://mlsanity.com/paper/1909.10094/) (2019)
- [Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource](https://mlsanity.com/paper/1804.06020/) (2018)
- [Rapid Customization for Event Extraction](https://mlsanity.com/paper/1809.07783/) (2018)

## Built on by (most cited first, 5 of 5)

- [Event Detection from Social Media for Epidemic Prediction](https://mlsanity.com/paper/2404.01679/) (2024)
- [SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness](https://mlsanity.com/paper/2410.18393/) (2024)
- [An overview of event extraction and its applications](https://mlsanity.com/paper/2111.03212/) (2021)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Syntactic-GCN Bert based Chinese Event Extraction](https://mlsanity.com/paper/2112.09939/) (2021)

## Abstract (arXiv)

Timely responses from policy makers to mitigate the impact of the COVID-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts. These events are reported at such a speed and scale as to be overwhelming. In this paper, we present ExcavatorCovid, a machine reading system that ingests open-source text documents (e.g., news and scientific publications), extracts COVID19 related events and relations between them, and builds a Temporal and Causal Analysis Graph (TCAG). Excavator will help government agencies alleviate the information overload, understand likely downstream effects of political and economic decisions and events related to the pandemic, and respond in a timely manner to mitigate the impact of COVID-19. We expect the utility of Excavator to outlive the COVID-19 pandemic: analysts and decision makers will be empowered by Excavator to better understand and solve complex problems in the future. An interactive TCAG visualization is available at http://afrl402.bbn.com:5050/index.html. We also released a demonstration video at https://vimeo.com/528619007.