MadDog: A Web-based System for Acronym Identification and Disambiguation
Not on the map. Why?
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 25 Jan 2021.
Builds on
9 of 32 references.
References · 9
- 2020Unsupervised Question Decomposition for Question Answeringcited by 118
- 2020Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoderscited by 61
- 2020What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguationcited by 26
- 2020MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretrainingcited by 18
- 2019Deep Contextualized Biomedical Abbreviation Expansioncited by 14
- 2019A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistencycited by 14
- 2020SemEval-2020 Task 6: Definition extraction from free text with the DEFT corpuscited by 12
- 2020Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understandingcited by 12
- 2019Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Controlcited by 1
Built on by
Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 0; 2025: 2.
8 papers.
- 2022Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processingcited by 23
- 2022SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptionscited by 6
- 2023GLADIS: A General and Large Acronym Disambiguation Benchmarkcited by 5
- 2022MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extractioncited by 4
- 2021SimCLAD: A Simple Framework for Contrastive Learning of Acronym Disambiguationcited by 3
- 2023Envisioning the Next-Gen Document Readercited by 0
- 2025Breaking the Curse of Knowledge: Designing Personalized Jargon Support for Real-Time Online Meetingscited by 0
- 2025DACE For Railway Acronym Disambiguationcited by 0
- 2025DACE For Railway Acronym Disambiguation20 Dec 2025
- 2025Breaking the Curse of Knowledge: Designing Personalized Jargon Support for Real-Time Online Meetings13 Aug 2025
- 2023Envisioning the Next-Gen Document Reader15 Feb 2023
- 2023GLADIS: A General and Large Acronym Disambiguation Benchmark3 Feb 2023
- 2022Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing28 Feb 2022
- 2022SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions19 Feb 2022
- 2022MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction19 Feb 2022
- 2021SimCLAD: A Simple Framework for Contrastive Learning of Acronym Disambiguation29 Nov 2021