# WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols

arXiv: https://arxiv.org/abs/2010.14576 · HTML: https://mlsanity.com/paper/2010.14576/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

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

- [Pre-train or Annotate? Domain Adaptation with a Constrained Budget](https://mlsanity.com/paper/2109.04711/) (2021)
- [A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processing](https://mlsanity.com/paper/2209.13136/) (2022)
- [To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning](https://mlsanity.com/paper/2104.08078/) (2021)
- [Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models](https://mlsanity.com/paper/2305.01645/) (2023)
- [SciDMT: A Large-Scale Corpus for Detecting Scientific Mentions](https://mlsanity.com/paper/2406.14756/) (2024)

## Abstract (arXiv)

This paper presents the results of the wet lab information extraction task at WNUT 2020. This task consisted of two sub tasks: (1) a Named Entity Recognition (NER) task with 13 participants and (2) a Relation Extraction (RE) task with 2 participants. We outline the task, data annotation process, corpus statistics, and provide a high-level overview of the participating systems for each sub task.