BFCAI at ComMA@ICON 2021: Support Vector Machines for Multilingual Gender Biased and Communal Language Identification

Fathy Elkazzaz, Fatma Sakr, Rasha Orban, Hamada Nayel

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.
Anthology ID:
2021.icon-multigen.11
Volume:
Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification
Month:
December
Year:
2021
Address:
NIT Silchar
Editors:
Ritesh Kumar, Siddharth Singh, Enakshi Nandi, Shyam Ratan, Laishram Niranjana Devi, Bornini Lahiri, Akanksha Bansal, Akash Bhagat, Yogesh Dawer
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
70–74
Language:
URL:
https://aclanthology.org/2021.icon-multigen.11
DOI:
Bibkey:
Cite (ACL):
Fathy Elkazzaz, Fatma Sakr, Rasha Orban, and Hamada Nayel. 2021. BFCAI at ComMA@ICON 2021: Support Vector Machines for Multilingual Gender Biased and Communal Language Identification. In Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification, pages 70–74, NIT Silchar. NLP Association of India (NLPAI).
Cite (Informal):
BFCAI at ComMA@ICON 2021: Support Vector Machines for Multilingual Gender Biased and Communal Language Identification (Elkazzaz et al., ICON 2021)
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