The neural network models work well in disambiguating abbreviations in clinical narratives, and they are robust across datasets. 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. The improvement was statistically significant at p < 0.05 on the auto-generated dataset. 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.ĬNN 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. 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. 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.Ĭonvolutional Neural Network (CNN) models were trained, one for each abbreviation, to disambiguate abbreviation senses. Abbreviations sense disambiguation is a special case of word sense disambiguation.

Atext abriviation