# Atypical lexical abbreviations identification in Russian medical texts

arXiv: https://arxiv.org/abs/2206.01987 · HTML: https://mlsanity.com/paper/2206.01987/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 4 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [Unsupervised Abbreviation Disambiguation Contextual disambiguation using word embeddings](https://mlsanity.com/paper/1904.00929/) (2019)
- [Building on Huang et al. GlossBERT for Word Sense Disambiguation](https://mlsanity.com/paper/2112.07089/) (2021)
- [Language Independent Acquisition of Abbreviations](https://mlsanity.com/paper/1709.08074/) (2017)

## Abstract (arXiv)

Abbreviation is a method of word formation that aims to construct the shortened term from the first letters of the initial phrase. Implicit abbreviations frequently cause the comprehension difficulties for unprepared readers. In this paper, we propose an efficient ML-based algorithm which allows to identify the abbreviations in Russian texts. The method achieves ROC AUC score 0.926 and F1 score 0.706 which are confirmed as competitive in comparison with the baselines. Along with the pipeline, we also establish first to our knowledge Russian dataset that is relevant for the desired task.