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 approachwhich originally utilized 20 teams over the course of several weeks -in 24 hours.
For example, SwellShark REF , specifically designed for biomedical NER, leverages a generative model to unify and model noise across different supervision sources for named entity typing.
SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data
ArXiv
ArXiv
Computer Science