Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Is artificial data useful for biomedical Natural Language Processing
  algorithms?   A major obstacle to the development of Natural Language Processing (NLP)
methods in the biomedical domain is data accessibility. This problem can be
addressed by generating medical data artificially. Most previous studies have
focused on the generation of short clinical text, and evaluation of the data
utility has been limited. We propose a generic methodology to guide the
generation of clinical text with key phrases. We use the artificial data as
additional training data in two key biomedical NLP tasks: text classification
and temporal relation extraction. We show that artificially generated training
data used in conjunction with real training data can lead to performance boosts
for data-greedy neural network algorithms. We also demonstrate the usefulness
of the generated data for NLP setups where it fully replaces real training
data.

14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio