@inproceedings{b-a-2021-ssncse,
title = "{SSNCSE}{\_}{NLP}@{D}ravidian{L}ang{T}ech-{EACL}2021: Offensive Language Identification on Multilingual Code Mixing Text",
author = "B, Bharathi and
A, Agnusimmaculate Silvia",
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-1.45",
pages = "313--318",
abstract = "Social networks made a huge impact in almost all fields in recent years. Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. Everyday we have to deal with texts, emails or different types of messages in which there are a variety of attacks and abusive phrases. It is the moderator{'}s decision which comments to remove from the platform because of violations and which ones to keep but an automatic software for detecting abusive languages would be useful in recent days. In this paper we describe an automatic offensive language identification from Dravidian languages with various machine learning algorithms. This is work is shared task in DravidanLangTech-EACL2021. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. This work explains the submissions made by SSNCSE{\_}NLP in DravidanLangTech-EACL2021 Code-mix tasks for Offensive language detection. We achieve F1 scores of 0.95 for Malayalam, 0.7 for Kannada and 0.73 for task2-Tamil on the test-set.",
}
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<abstract>Social networks made a huge impact in almost all fields in recent years. Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. Everyday we have to deal with texts, emails or different types of messages in which there are a variety of attacks and abusive phrases. It is the moderator’s decision which comments to remove from the platform because of violations and which ones to keep but an automatic software for detecting abusive languages would be useful in recent days. In this paper we describe an automatic offensive language identification from Dravidian languages with various machine learning algorithms. This is work is shared task in DravidanLangTech-EACL2021. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. This work explains the submissions made by SSNCSE_NLP in DravidanLangTech-EACL2021 Code-mix tasks for Offensive language detection. We achieve F1 scores of 0.95 for Malayalam, 0.7 for Kannada and 0.73 for task2-Tamil on the test-set.</abstract>
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%0 Conference Proceedings
%T SSNCSE_NLP@DravidianLangTech-EACL2021: Offensive Language Identification on Multilingual Code Mixing Text
%A B, Bharathi
%A A, Agnusimmaculate Silvia
%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 b-a-2021-ssncse
%X Social networks made a huge impact in almost all fields in recent years. Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. Everyday we have to deal with texts, emails or different types of messages in which there are a variety of attacks and abusive phrases. It is the moderator’s decision which comments to remove from the platform because of violations and which ones to keep but an automatic software for detecting abusive languages would be useful in recent days. In this paper we describe an automatic offensive language identification from Dravidian languages with various machine learning algorithms. This is work is shared task in DravidanLangTech-EACL2021. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. This work explains the submissions made by SSNCSE_NLP in DravidanLangTech-EACL2021 Code-mix tasks for Offensive language detection. We achieve F1 scores of 0.95 for Malayalam, 0.7 for Kannada and 0.73 for task2-Tamil on the test-set.
%U https://aclanthology.org/2021.dravidianlangtech-1.45
%P 313-318
Markdown (Informal)
[SSNCSE_NLP@DravidianLangTech-EACL2021: Offensive Language Identification on Multilingual Code Mixing Text](https://aclanthology.org/2021.dravidianlangtech-1.45) (B & A, DravidianLangTech 2021)
ACL