# MadDog: A Web-based System for Acronym Identification and Disambiguation

arXiv: https://arxiv.org/abs/2101.09893 · HTML: https://mlsanity.com/paper/2101.09893/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Unsupervised Question Decomposition for Question Answering](https://mlsanity.com/paper/2002.09758/) (2020)
- [Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoders](https://mlsanity.com/paper/2005.02590/) (2020)
- [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2010.14678/) (2020)
- [MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining](https://mlsanity.com/paper/2012.13978/) (2020)
- [Deep Contextualized Biomedical Abbreviation Expansion](https://mlsanity.com/paper/1906.03360/) (2019)
- [A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency](https://mlsanity.com/paper/1911.01678/) (2019)
- [SemEval-2020 Task 6: Definition extraction from free text with the DEFT corpus](https://mlsanity.com/paper/2008.13694/) (2020)
- [Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding](https://mlsanity.com/paper/2012.11760/) (2020)
- [Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control](https://mlsanity.com/paper/1907.03230/) (2019)

## Built on by (most cited first, 8 of 8)

- [Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing](https://mlsanity.com/paper/2203.00130/) (2022)
- [SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions](https://mlsanity.com/paper/2202.09695/) (2022)
- [GLADIS: A General and Large Acronym Disambiguation Benchmark](https://mlsanity.com/paper/2302.01860/) (2023)
- [MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction](https://mlsanity.com/paper/2202.09694/) (2022)
- [SimCLAD: A Simple Framework for Contrastive Learning of Acronym Disambiguation](https://mlsanity.com/paper/2111.14306/) (2021)
- [Envisioning the Next-Gen Document Reader](https://mlsanity.com/paper/2302.07492/) (2023)
- [Breaking the Curse of Knowledge: Designing Personalized Jargon Support for Real-Time Online Meetings](https://mlsanity.com/paper/2508.10239/) (2025)
- [DACE For Railway Acronym Disambiguation](https://mlsanity.com/paper/2512.18357/) (2025)

## Abstract (arXiv)

Acronyms and abbreviations are the short-form of longer phrases and they are ubiquitously employed in various types of writing. Despite their usefulness to save space in writing and reader's time in reading, they also provide challenges for understanding the text especially if the acronym is not defined in the text or if it is used far from its definition in long texts. To alleviate this issue, there are considerable efforts both from the research community and software developers to build systems for identifying acronyms and finding their correct meanings in the text. However, none of the existing works provide a unified solution capable of processing acronyms in various domains and to be publicly available. Thus, we provide the first web-based acronym identification and disambiguation system which can process acronyms from various domains including scientific, biomedical, and general domains. The web-based system is publicly available at http://iq.cs.uoregon.edu:5000 and a demo video is available at https://youtu.be/IkSh7LqI42M. The system source code is also available at https://github.com/amirveyseh/MadDog.