Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Structured prediction models for RNN based sequence labeling in clinical
  text   Sequence labeling is a widely used method for named entity recognition and
information extraction from unstructured natural language data. In clinical
domain one major application of sequence labeling involves extraction of
medical entities such as medication, indication, and side-effects from
Electronic Health Record narratives. Sequence labeling in this domain, presents
its own set of challenges and objectives. In this work we experimented with
various CRF based structured learning models with Recurrent Neural Networks. We
extend the previously studied LSTM-CRF models with explicit modeling of
pairwise potentials. We also propose an approximate version of skip-chain CRF
inference with RNN potentials. We use these methodologies for structured
prediction in order to improve the exact phrase detection of various medical
entities.

22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph