Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: De-identification of Unstructured Clinical Texts from Sequence to
  Sequence Perspective   In this work, we propose a novel problem formulation for de-identification of
unstructured clinical text. We formulate the de-identification problem as a
sequence to sequence learning problem instead of a token classification
problem. Our approach is inspired by the recent state-of -the-art performance
of sequence to sequence learning models for named entity recognition. Early
experimentation of our proposed approach achieved 98.91% recall rate on i2b2
dataset. This performance is comparable to current state-of-the-art models for
unstructured clinical text de-identification.

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The categories of this paper are: math
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The categories of this paper are: cmp-lg
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The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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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: patt-sol
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The categories of this paper are: econ
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The categories of this paper are: stat
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The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph