A split-and-match weak supervision pipeline trains BERT and BiLSTM models to extract and link medical entities from chief complaints without human annotation, achieving 67.5 F1 on a clinician-labeled test set.
Strong Heuristics for Named Entity Linking
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abstract
Named entity linking (NEL) in news is a challenging endeavour due to the frequency of unseen and emerging entities, which necessitates the use of unsupervised or zero-shot methods. However, such methods tend to come with caveats, such as no integration of suitable knowledge bases (like Wikidata) for emerging entities, a lack of scalability, and poor interpretability. Here, we consider person disambiguation in Quotebank, a massive corpus of speaker-attributed quotations from the news, and investigate the suitability of intuitive, lightweight, and scalable heuristics for NEL in web-scale corpora. Our best performing heuristic disambiguates 94% and 63% of the mentions on Quotebank and the AIDA-CoNLL benchmark, respectively. Additionally, the proposed heuristics compare favourably to the state-of-the-art unsupervised and zero-shot methods, Eigenthemes and mGENRE, respectively, thereby serving as strong baselines for unsupervised and zero-shot entity linking.
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Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints
A split-and-match weak supervision pipeline trains BERT and BiLSTM models to extract and link medical entities from chief complaints without human annotation, achieving 67.5 F1 on a clinician-labeled test set.