@inproceedings{elkazzaz-etal-2021-bfcai,
title = "{BFCAI} at {C}om{MA}@{ICON} 2021: Support Vector Machines for Multilingual Gender Biased and Communal Language Identification",
author = "Elkazzaz, Fathy and
Sakr, Fatma and
Orban, Rasha and
Nayel, Hamada",
editor = "Kumar, Ritesh and
Singh, Siddharth and
Nandi, Enakshi and
Ratan, Shyam and
Devi, Laishram Niranjana and
Lahiri, Bornini and
Bansal, Akanksha and
Bhagat, Akash and
Dawer, Yogesh",
booktitle = "Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification",
month = dec,
year = "2021",
address = "NIT Silchar",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2021.icon-multigen.11",
pages = "70--74",
abstract = "This paper presents the system that has been submitted to the multilingual gender biased and communal language identification shared task by BFCAI team. The proposed model used Support Vector Machines (SVMs) as a classification algorithm. The features have been extracted using TF/IDF model with unigram and bigram. The proposed model is very simple and there are no external resources are needed to build the model.",
}
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%0 Conference Proceedings
%T BFCAI at ComMA@ICON 2021: Support Vector Machines for Multilingual Gender Biased and Communal Language Identification
%A Elkazzaz, Fathy
%A Sakr, Fatma
%A Orban, Rasha
%A Nayel, Hamada
%Y Kumar, Ritesh
%Y Singh, Siddharth
%Y Nandi, Enakshi
%Y Ratan, Shyam
%Y Devi, Laishram Niranjana
%Y Lahiri, Bornini
%Y Bansal, Akanksha
%Y Bhagat, Akash
%Y Dawer, Yogesh
%S Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C NIT Silchar
%F elkazzaz-etal-2021-bfcai
%X This paper presents the system that has been submitted to the multilingual gender biased and communal language identification shared task by BFCAI team. The proposed model used Support Vector Machines (SVMs) as a classification algorithm. The features have been extracted using TF/IDF model with unigram and bigram. The proposed model is very simple and there are no external resources are needed to build the model.
%U https://aclanthology.org/2021.icon-multigen.11
%P 70-74
Markdown (Informal)
[BFCAI at ComMA@ICON 2021: Support Vector Machines for Multilingual Gender Biased and Communal Language Identification](https://aclanthology.org/2021.icon-multigen.11) (Elkazzaz et al., ICON 2021)
ACL