AI Article Synopsis
- The text introduces Latent Meaning Cells (LMC), a new deep learning model designed to create contextualized word representations by integrating local word context with metadata like section types and document IDs.
- The model is particularly useful in the clinical field, where the text is often semi-structured and covers a wide range of topics.
- In tests for zero-shot clinical acronym expansion on three datasets, the LMC outperformed various baseline models while requiring less pre-training, showcasing the importance of both metadata and the LMC's inference algorithm.
Article Abstract
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. The LMC significantly outperforms a diverse set of baselines at a fraction of the pre-training cost and learns clinically coherent representations. We demonstrate that not only is metadata itself very helpful for the task, but that the LMC inference algorithm provides an additional large benefit.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594244 | PMC |
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