Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Understanding patient complaint characteristics using contextual
  clinical BERT embeddings   In clinical conversational applications, extracted entities tend to capture
the main subject of a patient's complaint, namely symptoms or diseases.
However, they mostly fail to recognize the characterizations of a complaint
such as the time, the onset, and the severity. For example, if the input is "I
have a headache and it is extreme", state-of-the-art models only recognize the
main symptom entity - headache, but ignore the severity factor of "extreme",
that characterizes headache. In this paper, we design a two-stage approach to
detect the characterizations of entities like symptoms presented by general
users in contexts where they would describe their symptoms to a clinician. We
use Word2Vec and BERT to encode clinical text given by the patients. We
transform the output and re-frame the task as multi-label classification
problem. Finally, we combine the processed encodings with the Linear
Discriminant Analysis (LDA) algorithm to classify the characterizations of the
main entity. Experimental results demonstrate that our method achieves 40-50%
improvement on the accuracy over the state-of-the-art models.

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The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
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The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex