Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HS-BAN: A Benchmark Dataset of Social Media Comments for Hate Speech
  Detection in Bangla   In this paper, we present HS-BAN, a binary class hate speech (HS) dataset in
Bangla language consisting of more than 50,000 labeled comments, including
40.17% hate and rest are non hate speech. While preparing the dataset a strict
and detailed annotation guideline was followed to reduce human annotation bias.
The HS dataset was also preprocessed linguistically to extract different types
of slang currently people write using symbols, acronyms, or alternative
spellings. These slang words were further categorized into traditional and
non-traditional slang lists and included in the results of this paper. We
explored traditional linguistic features and neural network-based methods to
develop a benchmark system for hate speech detection for the Bangla language.
Our experimental results show that existing word embedding models trained with
informal texts perform better than those trained with formal text. Our
benchmark shows that a Bi-LSTM model on top of the FastText informal word
embedding achieved 86.78% F1-score. We will make the dataset available for
public use.

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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex