English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present an unsupervised context-sensitive spelling correction method forclinical free-text that uses word and character n-gram embeddings. Our methodgenerates misspelling replacement candidates and ranks them according to theirsemantic fit, by calculating a weighted cosine similarity between thevectorized representation of a candidate and the misspelling context. To tunethe parameters of this model, we generate self-induced spelling error corpora.We perform our experiments for two languages. For English, we greatlyoutperform off-the-shelf spelling correction tools on a manually annotatedMIMIC-III test set, and counter the frequency bias of a noisy channel model,showing that neural embeddings can be successfully exploited to improve uponthe state-of-the-art. For Dutch, we also outperform an off-the-shelf spellingcorrection tool on manually annotated clinical records from the AntwerpUniversity Hospital, but can offer no empirical evidence that our methodcounters the frequency bias of a noisy channel model in this case as well.However, both our context-sensitive model and our implementation of the noisychannel model obtain high scores on the test set, establishing astate-of-the-art for Dutch clinical spelling correction with the noisy channelmodel.

Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings