# Deep Contextualized Biomedical Abbreviation Expansion

arXiv: https://arxiv.org/abs/1906.03360 · HTML: https://mlsanity.com/paper/1906.03360/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2019.

## Built on by (most cited first, 14 of 14)

- [SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials](https://mlsanity.com/paper/2404.04963/) (2024)
- [NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports](https://mlsanity.com/paper/2305.03598/) (2023)
- [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)
- [SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data](https://mlsanity.com/paper/2305.02993/) (2023)
- [GLADIS: A General and Large Acronym Disambiguation Benchmark](https://mlsanity.com/paper/2302.01860/) (2023)
- [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)
- [ADBCMM : Acronym Disambiguation by Building Counterfactuals and Multilingual Mixing](https://mlsanity.com/paper/2112.08991/) (2021)
- [SciDr at SDU-2020: IDEAS -- Identifying and Disambiguating Everyday Acronyms for Scientific Domain](https://mlsanity.com/paper/2102.08818/) (2021)
- [ABB-BERT: A BERT model for disambiguating abbreviations and contractions](https://mlsanity.com/paper/2207.04008/) (2022)
- [Token Classification for Disambiguating Medical Abbreviations](https://mlsanity.com/paper/2210.02487/) (2022)
- [Dealing with Abbreviations in the Slovenian Biographical Lexicon](https://mlsanity.com/paper/2211.02429/) (2022)
- [DFKI-NLP at SemEval-2024 Task 2: Towards Robust LLMs Using Data Perturbations and MinMax Training](https://mlsanity.com/paper/2405.00321/) (2024)

## Abstract (arXiv)

Automatic identification and expansion of ambiguous abbreviations are essential for biomedical natural language processing applications, such as information retrieval and question answering systems. In this paper, we present DEep Contextualized Biomedical. Abbreviation Expansion (DECBAE) model. DECBAE automatically collects substantial and relatively clean annotated contexts for 950 ambiguous abbreviations from PubMed abstracts using a simple heuristic. Then it utilizes BioELMo to extract the contextualized features of words, and feed those features to abbreviation-specific bidirectional LSTMs, where the hidden states of the ambiguous abbreviations are used to assign the exact definitions. Our DECBAE model outperforms other baselines by large margins, achieving average accuracy of 0.961 and macro-F1 of 0.917 on the dataset. It also surpasses human performance for expanding a sample abbreviation, and remains robust in imbalanced, low-resources and clinical settings.