Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An analysis of full-size Russian complexly NER labelled corpus of
  Internet user reviews on the drugs based on deep learning and language neural
  nets   We present the full-size Russian complexly NER-labeled corpus of Internet
user reviews, along with an evaluation of accuracy levels reached on this
corpus by a set of advanced deep learning neural networks to extract the
pharmacologically meaningful entities from Russian texts. The corpus annotation
includes mentions of the following entities: Medication (33005 mentions),
Adverse Drug Reaction (1778), Disease (17403), and Note (4490). Two of them -
Medication and Disease - comprise a set of attributes. A part of the corpus has
the coreference annotation with 1560 coreference chains in 300 documents.
Special multi-label model based on a language model and the set of features is
developed, appropriate for presented corpus labeling. The influence of the
choice of different modifications of the models: word vector representations,
types of language models pre-trained for Russian, text normalization styles,
and other preliminary processing are analyzed. The sufficient size of our
corpus allows to study the effects of particularities of corpus labeling and
balancing entities in the corpus. As a result, the state of the art for the
pharmacological entity extraction problem for Russian is established on a
full-size labeled corpus. In case of the adverse drug reaction (ADR)
recognition, it is 61.1 by the F1-exact metric that, as our analysis shows, is
on par with the accuracy level for other language corpora with similar
characteristics and the ADR representativnes. The evaluated baseline precision
of coreference relation extraction on the corpus is 71, that is higher the
results reached on other Russian corpora.

25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an