Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SwellShark: A Generative Model for Biomedical Named Entity Recognition
  without Labeled Data   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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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-th
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con