A convolutional route to abbreviation disambiguation in clinical text
Objective: Abbreviations sense disambiguation is a special case of word sense disambiguation. Machine learning methods based on neural networks showed promising results for word sense disambiguation (Festag and Spreckelsen, 2017)  and, here we assess their effectiveness for abbreviation sense disambiguation. Methods: Convolutional Neural Network (CNN) models were trained, one for each abbreviation, to disambiguate abbreviation senses. A reverse substitution (of long forms with short forms) method from a previous study was used on clinical narratives from Cleveland Clinic, USA, to auto-generate training data. Accuracy of the CNN and traditional Support Vector Machine (SVM) models were studied using: (a) 5-fold cross validation on the auto-generated training data; (b) a manually created, set-aside gold standard; and (c) 10-fold cross validation on a publicly available dataset from a previous study. Results: CNN improved accuracy by 1–4 percentage points on all the three datasets compared to SVM, and the improvement was the most for the set-aside dataset. The improvement was statistically significant at p < 0.05 on the auto-generated dataset. We found that for some common abbreviations, sense distributions mismatch between the test and auto generated training data, and mitigating the mismatch significantly improved the model accuracy. Conclusion: The neural network models work well in disambiguating abbreviations in clinical narratives, and they are robust across datasets. This avoids feature-engineering for each dataset. Coupled with an enhanced auto-training data generation, neural networks can simplify development of a practical abbreviation disambiguation system.