Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Russian Jeopardy! Data Set for Question-Answering Systems   Question answering (QA) is one of the most common NLP tasks that relates to
named entity recognition, fact extraction, semantic search and some other
fields. In industry, it is much appreciated in chatbots and corporate
information systems. It is also a challenging task that attracted the attention
of a very general audience at the quiz show Jeopardy! In this article we
describe a Jeopardy!-like Russian QA data set collected from the official
Russian quiz database Chgk (che ge ka). The data set includes 379,284 quiz-like
questions with 29,375 from the Russian analogue of Jeopardy! - "Own Game". We
observe its linguistic features and the related QA-task. We conclude about
perspectives of a QA competition based on the data set collected from this
database.

12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph