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SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data

Abstract · Apr 20, 2017 23:02 ·

taggers swellshark weak supervision hand labeled biomedical ner labeling resources cs-cl

Arxiv Abstract

  • Jason Fries
  • Sen Wu
  • Alex Ratner
  • Christopher Ré

We present SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data. Our approach views biomedical resources like lexicons as function primitives for autogenerating weak supervision. We then use a generative model to unify and denoise this supervision and construct large-scale, probabilistically labeled datasets for training high-accuracy NER taggers. In three biomedical NER tasks, SwellShark achieves competitive scores with state-of-the-art supervised benchmarks using no hand-labeled training data. In a drug name extraction task using patient medical records, one domain expert using SwellShark achieved within 5.1% of a crowdsourced annotation approach – which originally utilized 20 teams over the course of several weeks – in 24 hours.

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