# Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding

arXiv: https://arxiv.org/abs/2012.11760 · HTML: https://mlsanity.com/paper/2012.11760/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [MadDog: A Web-based System for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2101.09893/) (2021)
- [BERT-based Acronym Disambiguation with Multiple Training Strategies](https://mlsanity.com/paper/2103.00488/) (2021)
- [AT-BERT: Adversarial Training BERT for Acronym Identification Winning Solution for SDU@AAAI-21](https://mlsanity.com/paper/2101.03700/) (2021)
- [SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions](https://mlsanity.com/paper/2202.09695/) (2022)
- [Leveraging Domain Agnostic and Specific Knowledge for Acronym Disambiguation](https://mlsanity.com/paper/2107.00316/) (2021)
- [SimCLAD: A Simple Framework for Contrastive Learning of Acronym Disambiguation](https://mlsanity.com/paper/2111.14306/) (2021)
- [PSG: Prompt-based Sequence Generation for Acronym Extraction](https://mlsanity.com/paper/2111.14301/) (2021)
- [SciDr at SDU-2020: IDEAS -- Identifying and Disambiguating Everyday Acronyms for Scientific Domain](https://mlsanity.com/paper/2102.08818/) (2021)
- [CABACE: Injecting Character Sequence Information and Domain Knowledge for Enhanced Acronym and Long-Form Extraction](https://mlsanity.com/paper/2112.13237/) (2021)
- [Envisioning the Next-Gen Document Reader](https://mlsanity.com/paper/2302.07492/) (2023)
- [Preface to the Special Issue of the TAL Journal on Scholarly Document Processing](https://mlsanity.com/paper/2506.03587/) (2025)
- [DACE For Railway Acronym Disambiguation](https://mlsanity.com/paper/2512.18357/) (2025)

## Abstract (arXiv)

Acronyms are the short forms of longer phrases and they are frequently used in writing, especially scholarly writing, to save space and facilitate the communication of information. As such, every text understanding tool should be capable of recognizing acronyms in text (i.e., acronym identification) and also finding their correct meaning (i.e., acronym disambiguation). As most of the prior works on these tasks are restricted to the biomedical domain and use unsupervised methods or models trained on limited datasets, they fail to perform well for scientific document understanding. To push forward research in this direction, we have organized two shared task for acronym identification and acronym disambiguation in scientific documents, named AI@SDU and AD@SDU, respectively. The two shared tasks have attracted 52 and 43 participants, respectively. While the submitted systems make substantial improvements compared to the existing baselines, there are still far from the human-level performance. This paper reviews the two shared tasks and the prominent participating systems for each of them.