Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based
  Approach to Offensive Language Identification   This paper presents the models submitted by Ghmerti team for subtasks A and B
of the OffensEval shared task at SemEval 2019. OffensEval addresses the problem
of identifying and categorizing offensive language in social media in three
subtasks; whether or not a content is offensive (subtask A), whether it is
targeted (subtask B) towards an individual, a group, or other entities (subtask
C). The proposed approach includes character-level Convolutional Neural
Network, word-level Recurrent Neural Network, and some preprocessing. The
performance achieved by the proposed model for subtask A is 77.93%
macro-averaged F1-score.

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The categories of this paper are: stat
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
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The categories of this paper are: eess
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg
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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an