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Abbreviations and contractions are commonly found in text across differentdomains. For example, doctors' notes contain many contractions that can bepersonalized based on their choices. Existing spelling correction models arenot suitable to handle expansions because of many reductions of characters inwords. In this work, we propose ABB-BERT, a BERT-based model, which deals withan ambiguous language containing abbreviations and contractions. ABB-BERT canrank them from thousands of options and is designed for scale. It is trained onWikipedia text, and the algorithm allows it to be fine-tuned with littlecompute to get better performance for a domain or person. We are publiclyreleasing the training dataset for abbreviations and contractions derived fromWikipedia.

ABB-BERT: A BERT model for disambiguating abbreviations and contractions