Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence
Abstract
Spelling correction is a particularly important problem in clinical natural language processing because of the abundant occurrence of misspellings in medical records. However, the scarcity of labeled datasets in a clinical context makes it hard to build a machine learning system for such clinical spelling correction. In this work, we present a probabilistic model of correcting misspellings based on a simple conditional independence assumption, which leads to a modular decomposition into a language model and a corruption model. With a deep character-level language model trained on a large clinical corpus, and a simple edit-based corruption model, we can build a spelling correction model with small or no real data. Experimental results show that our model significantly outperforms baselines on two healthcare spelling correction datasets.
Cite
Text
Kim et al. "Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence." Proceedings of the Conference on Health, Inference, and Learning, 2022.Markdown
[Kim et al. "Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence." Proceedings of the Conference on Health, Inference, and Learning, 2022.](https://mlanthology.org/chil/2022/kim2022chil-contextsensitive/)BibTeX
@inproceedings{kim2022chil-contextsensitive,
title = {{Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence}},
author = {Kim, Juyong and Weiss, Jeremy C and Ravikumar, Pradeep},
booktitle = {Proceedings of the Conference on Health, Inference, and Learning},
year = {2022},
pages = {234-247},
volume = {174},
url = {https://mlanthology.org/chil/2022/kim2022chil-contextsensitive/}
}