Medical entity linking is the task of identifying and standardizing concepts referred in a scientific article or clinical record. Existing methods adopt a two-step approach of detecting mentions and identifying a list of candidate concepts for them. In this paper, we probe the impact of incorporating an entity disambiguation step in existing entity linkers . 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. We make our source code and datasets publicly available for medical entity linking research.
Medical entity linking is the task of identifying and standardizing medical concepts referred to in an unstructured text. Most of the existing methods adopt a three-step approach of (1) detecting mentions, (2) generating a list of candidate concepts , and finally (3) picking the best concept among them. In this paper, we probe into alleviating the problem of overgeneration of candidate concepts in the candidate generation module, the most under-studied component of medical entity linking . For this, we present MedType, a fully modular system that prunes out irrelevant candidate concepts based on the predicted 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. We make our source code and datasets publicly available for medical entity linking research.
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concepts referred in a scientific article or clinical record. Existing
medical concepts referred to in an unstructured text. Most of the existing
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two-step approach of detecting mentions and identifying
three-step approach of (1) detecting mentions, (2) generating
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for
, and finally (3) picking the best concept among
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the impact of incorporating an entity disambiguation step in existing entity linkers
into alleviating the problem of overgeneration of candidate concepts in the candidate generation module, the most under-studied component of medical entity linking
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novel method that leverages the surrounding context to identify the
fully modular system that prunes out irrelevant candidate concepts based on the predicted
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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
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