Before revision:  For this, we present MedType, a novel method that leverages the surrounding context to identify the semantic type of a mentionand uses it for filtering out candidate concepts of the wrong types. We further present two novel largescale, automatically-created datasetsof medical entity mentions: WIKIMED, a Wikipediabased dataset for cross-domain transfer learning, and PUBMEDDS, a distantly-supervised dataset of medical entity mentions in biomedical abstracts. Through extensive experiments across several datasetsand methods, we demonstrate that MedType pre-trained on our proposed datasets substantially improve medical entity linking and gives state-of-the-art performance.
After revision:  For this, we present MedType, a novel method that leverages the surrounding context to identify the semantic type of an entity mention. We incorporate MedType into five off-the-shelf toolkits for medical entity linking and demonstrate that it consistently improves entity linking performance across several benchmark datasets. To address the dearth of annotated training data for medical entity linking, we present WikiMed and PubMedDS, two large-scale medical entity linking datasets, and demonstrate that pre-training MedType on these datasets further improves entity linking performance.