@inproceedings{cuellar-hidalgo-etal-2021-luc,
title = "{LUC} at {C}om{MA}-2021 Shared Task: Multilingual Gender Biased and Communal Language Identification without Using Linguistic Features",
author = "Cu{\'e}llar-Hidalgo, Rodrigo and
Guerrero-Zambrano, Julio de Jes{\'u}s and
Forest, Dominic and
Reyes-Salgado, Gerardo and
Torres-Moreno, Juan-Manuel",
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.6",
pages = "41--45",
abstract = "This work aims to evaluate the ability that both probabilistic and state-of-the-art vector space modeling (VSM) methods provide to well known machine learning algorithms to identify social network documents to be classified as aggressive, gender biased or communally charged. To this end, an exploratory stage was performed first in order to find relevant settings to test, i.e. by using training and development samples, we trained multiple algorithms using multiple vector space modeling and probabilistic methods and discarded the less informative configurations. These systems were submitted to the competition of the ComMA@ICON{'}21 Workshop on Multilingual Gender Biased and Communal Language Identification.",
}
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%0 Conference Proceedings
%T LUC at ComMA-2021 Shared Task: Multilingual Gender Biased and Communal Language Identification without Using Linguistic Features
%A Cuéllar-Hidalgo, Rodrigo
%A Guerrero-Zambrano, Julio de Jesús
%A Forest, Dominic
%A Reyes-Salgado, Gerardo
%A Torres-Moreno, Juan-Manuel
%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 cuellar-hidalgo-etal-2021-luc
%X This work aims to evaluate the ability that both probabilistic and state-of-the-art vector space modeling (VSM) methods provide to well known machine learning algorithms to identify social network documents to be classified as aggressive, gender biased or communally charged. To this end, an exploratory stage was performed first in order to find relevant settings to test, i.e. by using training and development samples, we trained multiple algorithms using multiple vector space modeling and probabilistic methods and discarded the less informative configurations. These systems were submitted to the competition of the ComMA@ICON’21 Workshop on Multilingual Gender Biased and Communal Language Identification.
%U https://aclanthology.org/2021.icon-multigen.6
%P 41-45
Markdown (Informal)
[LUC at ComMA-2021 Shared Task: Multilingual Gender Biased and Communal Language Identification without Using Linguistic Features](https://aclanthology.org/2021.icon-multigen.6) (Cuéllar-Hidalgo et al., ICON 2021)
ACL