Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Effective Feature Representation for Clinical Text Concept Extraction   Crucial information about the practice of healthcare is recorded only in
free-form text, which creates an enormous opportunity for high-impact NLP.
However, annotated healthcare datasets tend to be small and expensive to
obtain, which raises the question of how to make maximally efficient uses of
the available data. To this end, we develop an LSTM-CRF model for combining
unsupervised word representations and hand-built feature representations
derived from publicly available healthcare ontologies. We show that this
combined model yields superior performance on five datasets of diverse kinds of
healthcare text (clinical, social, scientific, commercial). Each involves the
labeling of complex, multi-word spans that pick out different healthcare
concepts. We also introduce a new labeled dataset for identifying the treatment
relations between drugs and diseases.

25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math