Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Empirical Evaluation of Text Representation Schemes on Multilingual
  Social Web to Filter the Textual Aggression   This paper attempt to study the effectiveness of text representation schemes
on two tasks namely: User Aggression and Fact Detection from the social media
contents. In User Aggression detection, The aim is to identify the level of
aggression from the contents generated in the Social media and written in the
English, Devanagari Hindi and Romanized Hindi. Aggression levels are
categorized into three predefined classes namely: `Non-aggressive`, `Overtly
Aggressive`, and `Covertly Aggressive`. During the disaster-related incident,
Social media like, Twitter is flooded with millions of posts. In such emergency
situations, identification of factual posts is important for organizations
involved in the relief operation. We anticipated this problem as a combination
of classification and Ranking problem. This paper presents a comparison of
various text representation scheme based on BoW techniques, distributed
word/sentence representation, transfer learning on classifiers. Weighted $F_1$
score is used as a primary evaluation metric. Results show that text
representation using BoW performs better than word embedding on machine
learning classifiers. While pre-trained Word embedding techniques perform
better on classifiers based on deep neural net. Recent transfer learning model
like ELMO, ULMFiT are fine-tuned for the Aggression classification task.
However, results are not at par with pre-trained word embedding model. Overall,
word embedding using fastText produce best weighted $F_1$-score than Word2Vec
and Glove. Results are further improved using pre-trained vector model.
Statistical significance tests are employed to ensure the significance of the
classification results. In the case of lexically different test Dataset, other
than training Dataset, deep neural models are more robust and perform
substantially better than machine learning classifiers.

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The categories of this paper are: q-alg
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The categories of this paper are: nlin
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The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: math
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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin