# Mining Tortured Abbreviations From The Scientific Literature: From Filtering Rules To Language Models

Alexandre Clausse, Guillaume Cabanac, Pascal Cuxac, Ophélie Fraisier-Vannier, Cyril Labbé

## ► To cite this version:

Alexandre Clausse, Guillaume Cabanac, Pascal Cuxac, Ophélie Fraisier-Vannier, Cyril Labbé. Mining Tortured Abbreviations From The Scientific Literature: From Filtering Rules To Language Models. 2025. hal-05443430

HAL Id: hal-05443430

https://hal.science/hal-05443430v1

Preprint submitted on 6 Jan 2026

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# Mining Tortured Abbreviations From The Scientific Literature:
## From Filtering Rules To Language Models

Alexandre CLAUSSE¹, Guillaume CABANAC¹, ², Pascal CUXAC³, OPHÉLIE FRAISIER-VANNIER¹, and
Cyril LABBÉ⁴

¹ University of Toulouse, IRIT UMR 5505, Toulouse, France

² Institut universitaire de France (IUF), Paris, France

³ INIST - CNRS, UAR76, Vandœuvre-lès-Nancy, France

⁴ Grenoble Alpes University, CNRS, Grenoble INP, LIG, 38000 Grenoble, France

**Objective.** The 'Tortured Abbreviations Miner' (TAM, WCRI'24, https://hal.science/hal-04311600) supports the human re-assessment of unreliable scientific articles. It detects tortured abbreviations: established scientific concepts paraphrased with synonyms, that do not match their abbreviation (e.g., 'Convolutional Brain Organisation (CNN)' for 'Convolutional Neural Network (CNN).') This abstract presents a new method based on language models to improve TAM performance through the identification and classification of all abbreviations from any given article.

**Method.** We built a test collection by sampling 150 tortured engineering articles in open access. The first author highlighted and extracted all abbreviations from each article. Each abbreviation was labelled as tortured or genuine. The first 75 articles come from the Problematic Paper Screener (PPS, WCRI'22, https://arxiv.org/abs/2210.04895) and have been used to develop the TAM classifier (80% to train, 10% to test, and 10% to validate language models). The next 75 articles were published by Hindawi (https://doi.org/10.5281/zenodo.10419036) and have been used to test the method in simulated conditions: 25 articles without any tortured abbreviation; the top 25 most tortured articles flagged by the PPS; and 25 randomly selected articles. We benchmarked the algorithm's output to compute recall and precision scores.

**Results.** The publicly released test collection (https://doi.org/10.5281/zenodo.14002956) contains 2,029 abbreviations including 680 tortured abbreviations. The improved TAM yields 53% precision and 72% recall, compared to the 61% precision and 27% recall in the previous version. Incremental failure analysis identifies false positives (e.g., examples given in brackets and references) and false negatives (i.e., matching acronym-initials yet irrelevant).

**Conclusion.** TAM allows the screening of incoming manuscripts or peer-reviewed articles to spot unreliable passages. Publishers are encouraged to add this screener in their editorial workflow, scientific sleuths can use it to screen articles for post-publication review. We welcome developers to use the test collection to benchmark their own implementations and compare to the baseline performance reported in this WCRI abstract. Future work will 1) rely on language models for both abbreviation extraction and classification, using 10-fold cross validation, and 2) focus on using domain-specific datasets.

**Funding.** The NanoBubbles project has received Synergy grant from the European Research Council (ERC, EU's H2020 program, agreement no. 951393).