deepBioWSD: effective deep neural word sense disambiguation of biomedical text data

Journal of the American Medical Informatics Association : JAMIA
Ahmad PesaranghaderAli Pesaranghader

Abstract

In biomedicine, there is a wealth of information hidden in unstructured narratives such as research articles and clinical reports. To exploit these data properly, a word sense disambiguation (WSD) algorithm prevents downstream difficulties in the natural language processing applications pipeline. Supervised WSD algorithms largely outperform un- or semisupervised and knowledge-based methods; however, they train 1 separate classifier for each ambiguous term, necessitating a large number of expert-labeled training data, an unattainable goal in medical informatics. To alleviate this need, a single model that shares statistical strength across all instances and scales well with the vocabulary size is desirable. Built on recent advances in deep learning, our deepBioWSD model leverages 1 single bidirectional long short-term memory network that makes sense prediction for any ambiguous term. In the model, first, the Unified Medical Language System sense embeddings will be computed using their text definitions; and then, after initializing the network with these embeddings, it will be trained on all (available) training data collectively. This method also considers a novel technique for automatic collection of training data from PubMed t...Continue Reading

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Citations

May 31, 2020·Journal of the American Medical Informatics Association : JAMIA·Chris J LuJames G Mork
Sep 29, 2020·Frontiers in Cell and Developmental Biology·Nadeesha PereraFrank Emmert-Streib
Dec 16, 2020·Journal of the American Medical Informatics Association : JAMIA·Denis Newman-GriffisEric Fosler-Lussier
Jun 4, 2021·Scientific Data·Lisa Grossman LiuDavid K Vawdrey
Aug 15, 2021·Journal of Biomedical Informatics·Shikhar VashishthCarolyn P Rosé

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