Zero-Shot Clinical Acronym Expansion via Latent Meaning Cells

Understand

We introduce Latent Meaning Cells, a deep latent variable model which learns contextualized representations of words by combining local lexical context and metadata.

• Metadata can refer to granular context, such as section type, or to more global context, such as unique document ids.
• Reliance on metadata for contextualized representation learning is apropos in the clinical domain where text is semi-structured and expresses high variation in topics.
• We evaluate the LMC model on the task of zero-shot clinical acronym expansion across three datasets.