WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Oct 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1.
5 papers.
- 2021Pre-train or Annotate? Domain Adaptation with a Constrained Budgetcited by 27
- 2022A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processingcited by 22
- 2021To Share or not to Share: Predicting Sets of Sources for Model Transfer Learningcited by 11
- 2023Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Modelscited by 11
- 2024SciDMT: A Large-Scale Corpus for Detecting Scientific Mentionscited by 6
- 2024SciDMT: A Large-Scale Corpus for Detecting Scientific Mentions20 Jun 2024
- 2023Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models2 May 2023
- 2022A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processing27 Sep 2022
- 2021Pre-train or Annotate? Domain Adaptation with a Constrained Budget10 Sep 2021
- 2021To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning16 Apr 2021