## A simple algorithm for identifying abbreviation definitions in biomedical text > [!Abstract]- > The volume of biomedical text is growing at a fast rate, creating challenges for humans and computer systems alike. One of these challenges arises from the frequent use of novel abbreviations in these texts, thus requiring that biomedical lexical ontologies be continually updated. In this paper we show that the problem of identifying abbreviations’ definitions can be solved with a much simpler algorithm than that proposed by other research efforts. The algorithm achieves 96% precision and 82% recall on a standard test collection, which is at least as good as existing approaches. It also achieves 95% precision and 82% recall on another, larger test set. A notable advantage of the algorithm is that, unlike other approaches, it does not require any training data. > [!Cite]- > Schwartz, Ariel S., and Marti A. Hearst. “A Simple Algorithm for Identifying Abbreviation Definitions in Biomedical Text.” In _Biocomputing 2003_. WORLD SCIENTIFIC, 2002. [https://doi.org/10.1142/9789812776303_0042](https://doi.org/10.1142/9789812776303_0042). > > [link](https://www.worldscientific.com/doi/abs/10.1142/9789812776303_0042) [online](http://zotero.org/users/17587716/items/9VGTP9IJ) [local](zotero://select/library/items/9VGTP9IJ) [pdf](file://C:\Users\erikt\Zotero\storage\77ULUGLG\Schwartz%20and%20Hearst%20-%202002%20-%20A%20simple%20algorithm%20for%20identifying%20abbreviation%20definitions%20in%20biomedical%20text.pdf) ## Notes %% begin notes %% Canonical reference for pattern matching algorithm to detect acroyms. Implemented in `scispacy` (but due to dependency issues I recommend just copy/pasting the Abbreviations module into any necessary projects). %% end notes %% %% begin annotations %% %% end annotations %% %% Import Date: 2025-10-20T11:48:16.348-06:00 %%