@inproceedings{j-hs-2021-trollmeta,
title = "{T}roll{M}eta@{D}ravidian{L}ang{T}ech-{EACL}2021: Meme classification using deep learning",
author = "J, Manoj Balaji and
Hs, Chinmaya",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Kumar M, Anand and
Krishnamurthy, Parameswari and
Sherly, Elizabeth",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.dravidianlangtech-1.39",
pages = "277--280",
abstract = "Memes act as a medium to carry one{'}s feelings, cultural ideas, or practices by means of symbols, imitations, or simply images. Whenever social media is involved, hurting the feelings of others and abusing others are always a problem. Here we are proposing a system, that classifies the memes into abusive/offensive memes and neutral ones. The work involved classifying the images into offensive and non-offensive ones. The system implements resnet-50, a deep residual neural network architecture.",
}
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<abstract>Memes act as a medium to carry one’s feelings, cultural ideas, or practices by means of symbols, imitations, or simply images. Whenever social media is involved, hurting the feelings of others and abusing others are always a problem. Here we are proposing a system, that classifies the memes into abusive/offensive memes and neutral ones. The work involved classifying the images into offensive and non-offensive ones. The system implements resnet-50, a deep residual neural network architecture.</abstract>
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%0 Conference Proceedings
%T TrollMeta@DravidianLangTech-EACL2021: Meme classification using deep learning
%A J, Manoj Balaji
%A Hs, Chinmaya
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Kumar M, Anand
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%S Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv
%F j-hs-2021-trollmeta
%X Memes act as a medium to carry one’s feelings, cultural ideas, or practices by means of symbols, imitations, or simply images. Whenever social media is involved, hurting the feelings of others and abusing others are always a problem. Here we are proposing a system, that classifies the memes into abusive/offensive memes and neutral ones. The work involved classifying the images into offensive and non-offensive ones. The system implements resnet-50, a deep residual neural network architecture.
%U https://aclanthology.org/2021.dravidianlangtech-1.39
%P 277-280
Markdown (Informal)
[TrollMeta@DravidianLangTech-EACL2021: Meme classification using deep learning](https://aclanthology.org/2021.dravidianlangtech-1.39) (J & Hs, DravidianLangTech 2021)
ACL