We present a novel resource to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. This repository contains models and datasets described in our paper: A Search Engine for Discovery of Scientific Challenges and Directions. Check out our search engine!
- Please cite our paper if you use our datasets or models in your project. See the BibTeX.
- Feel free to email us.
The train, test, and val csvs are can be downloaded from huggingface. You can download a pre-trained model from here.
We include an example notebook that uses the model for inference. See Inference_Notebook.ipynb.
If using our dataset and models, please cite:
@misc{lahav2021search,
title={A Search Engine for Discovery of Scientific Challenges and Directions},
author={Dan Lahav and Jon Saad Falcon and Bailey Kuehl and Sophie Johnson and Sravanthi Parasa and Noam Shomron and Duen Horng Chau and Diyi Yang and Eric Horvitz and Daniel S. Weld and Tom Hope},
year={2021},
eprint={2108.13751},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Please don't hesitate to reach out.
Email: lahav@mail.tau.ac.il,tomh@allenai.org.