Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Negation Detection for Clinical Text Mining in Russian   Developing predictive modeling in medicine requires additional features from
unstructured clinical texts. In Russia, there are no instruments for natural
language processing to cope with problems of medical records. This paper is
devoted to a module of negation detection. The corpus-free machine learning
method is based on gradient boosting classifier is used to detect whether a
disease is denied, not mentioned or presented in the text. The detector
classifies negations for five diseases and shows average F-score from 0.81 to
0.93. The benefits of negation detection have been demonstrated by predicting
the presence of surgery for patients with the acute coronary syndrome.

34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio