Dominic Forest


2021

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LUC at ComMA-2021 Shared Task: Multilingual Gender Biased and Communal Language Identification without Using Linguistic Features
Rodrigo Cuéllar-Hidalgo | Julio de Jesús Guerrero-Zambrano | Dominic Forest | Gerardo Reyes-Salgado | Juan-Manuel Torres-Moreno
Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification

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.

2018

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Cyberbullying Detection Task: the EBSI-LIA-UNAM System (ELU) at COLING’18 TRAC-1
Ignacio Arroyo-Fernández | Dominic Forest | Juan-Manuel Torres-Moreno | Mauricio Carrasco-Ruiz | Thomas Legeleux | Karen Joannette
Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)

The phenomenon of cyberbullying has growing in worrying proportions with the development of social networks. Forums and chat rooms are spaces where serious damage can now be done to others, while the tools for avoiding on-line spills are still limited. This study aims to assess the ability that both classical and state-of-the-art vector space modeling methods provide to well known learning machines to identify aggression levels in social network cyberbullying (i.e. social network posts manually labeled as Overtly Aggressive, Covertly Aggressive and Non-aggressive). 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 learning machines using multiple vector space modeling methods and discarded the less informative configurations. Finally, we selected the two best settings and their voting combination to form three competing systems. These systems were submitted to the competition of the TRACK-1 task of the Workshop on Trolling, Aggression and Cyberbullying. Our voting combination system resulted second place in predicting Aggression levels on a test set of untagged social network posts.

2014

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Music period detection of music collections using learning techniques (Détection de périodes musicales d’une collection de musique par apprentissage) [in French]
Rémy Kessler | Nicolas Béchet | Audrey Laplante | Dominic Forest
Proceedings of TALN 2014 (Volume 2: Short Papers)

2012

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JEP-TALN-RECITAL 2012, Workshop DEFT 2012: DÉfi Fouille de Textes (DEFT 2012 Workshop: Text Mining Challenge)
Cyril Grouin | Dominic Forest | Gilles Sérasset
JEP-TALN-RECITAL 2012, Workshop DEFT 2012: DÉfi Fouille de Textes (DEFT 2012 Workshop: Text Mining Challenge)

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Indexation libre et contrôlée d’articles scientifiques. Présentation et résultats du défi fouille de textes DEFT2012 (Controlled and free indexing of scientific papers. Presentation and results of the DEFT2012 text-mining challenge) [in French]
Patrick Paroubek | Pierre Zweigenbaum | Dominic Forest | Cyril Grouin
JEP-TALN-RECITAL 2012, Workshop DEFT 2012: DÉfi Fouille de Textes (DEFT 2012 Workshop: Text Mining Challenge)