Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Question Answering based Clinical Text Structuring Using Pre-trained
  Language Model   Clinical text structuring is a critical and fundamental task for clinical
research. Traditional methods such as taskspecific end-to-end models and
pipeline models usually suffer from the lack of dataset and error propagation.
In this paper, we present a question answering based clinical text structuring
(QA-CTS) task to unify different specific tasks and make dataset shareable. A
novel model that aims to introduce domain-specific features (e.g., clinical
named entity information) into pre-trained language model is also proposed for
QA-CTS task. Experimental results on Chinese pathology reports collected from
Ruijing Hospital demonstrate our presented QA-CTS task is very effective to
improve the performance on specific tasks. Our proposed model also competes
favorably with strong baseline models in specific tasks.

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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: math
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The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: physics
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The categories of this paper are: nucl-th
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The categories of this paper are: eess