# Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned

arXiv: https://arxiv.org/abs/2004.05125 · HTML: https://mlsanity.com/paper/2004.05125/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic](https://mlsanity.com/paper/2106.03794/) (2021)
- [MS MARCO: Benchmarking Ranking Models in the Large-Data Regime](https://mlsanity.com/paper/2105.04021/) (2021)
- [Yes, BM25 is a Strong Baseline for Legal Case Retrieval](https://mlsanity.com/paper/2105.05686/) (2021)
- [In Defense of Cross-Encoders for Zero-Shot Retrieval](https://mlsanity.com/paper/2212.06121/) (2022)
- [No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval](https://mlsanity.com/paper/2206.02873/) (2022)
- [Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task](https://mlsanity.com/paper/2205.15172/) (2022)
- [Good Data, Large Data, or No Data? Comparing Three Approaches in Developing Research Aspect Classifiers for Biomedical Papers](https://mlsanity.com/paper/2306.04820/) (2023)
- [COVIDRead: A Large-scale Question Answering Dataset on COVID-19](https://mlsanity.com/paper/2110.09321/) (2021)
- [A Practical Entity Linking System for Tables in Scientific Literature](https://mlsanity.com/paper/2306.10044/) (2023)
- [Health Misinformation in Social Networks: A Survey of IT Approaches](https://mlsanity.com/paper/2410.18670/) (2024)

## Abstract (arXiv)

We present the Neural Covidex, a search engine that exploits the latest neural ranking architectures to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. This web application exists as part of a suite of tools that we have developed over the past few weeks to help domain experts tackle the ongoing global pandemic. We hope that improved information access capabilities to the scientific literature can inform evidence-based decision making and insight generation. This paper describes our initial efforts and offers a few thoughts about lessons we have learned along the way.