Juan Vásquez

Also published as: Juan Vasquez


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HOMO-MEX: A Mexican Spanish Annotated Corpus for LGBT+phobia Detection on Twitter
Juan Vásquez | Scott Andersen | Gemma Bel-enguix | Helena Gómez-adorno | Sergio-luis Ojeda-trueba
The 7th Workshop on Online Abuse and Harms (WOAH)

In the past few years, the NLP community has actively worked on detecting LGBT+Phobia in online spaces, using textual data publicly available Most of these are for the English language and its variants since it is the most studied language by the NLP community. Nevertheless, efforts towards creating corpora in other languages are active worldwide. Despite this, the Spanish language is an understudied language regarding digital LGBT+Phobia. The only corpus we found in the literature was for the Peninsular Spanish dialects, which use LGBT+phobic terms different than those in the Mexican dialect. For this reason, we present Homo-MEX, a novel corpus for detecting LGBT+Phobia in Mexican Spanish. In this paper, we describe our data-gathering and annotation process. Also, we present a classification benchmark using various traditional machine learning algorithms and two pre-trained deep learning models to showcase our corpus classification potential.

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Classifying Organized Criminal Violence in Mexico using ML and LLMs
Javier Osorio | Juan Vasquez
Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text

Natural Language Processing (NLP) tools have been rapidly adopted in political science for the study of conflict and violence. In this paper, we present an application to analyze various lethal and non-lethal events conducted by organized criminal groups and state forces in Mexico. Based on a large corpus of news articles in Spanish and a set of high-quality annotations, the application evaluates different Machine Learning (ML) algorithms and Large Language Models (LLMs) to classify documents and individual sentences, and to identify specific behaviors related to organized criminal violence and law enforcement efforts. Our experiments support the growing evidence that BERT-like models achieve outstanding classification performance for the study of organized crime. This application amplifies the capacity of conflict scholars to provide valuable information related to important security challenges in the developing world.


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HeteroCorpus: A Corpus for Heteronormative Language Detection
Juan Vásquez | Gemma Bel-Enguix | Scott Thomas Andersen | Sergio-Luis Ojeda-Trueba
Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

In recent years, plenty of work has been done by the NLP community regarding gender bias detection and mitigation in language systems. Yet, to our knowledge, no one has focused on the difficult task of heteronormative language detection and mitigation. We consider this an urgent issue, since language technologies are growing increasingly present in the world and, as it has been proven by various studies, NLP systems with biases can create real-life adverse consequences for women, gender minorities and racial minorities and queer people. For these reasons, we propose and evaluate HeteroCorpus; a corpus created specifically for studying heterononormative language in English. Additionally, we propose a baseline set of classification experiments on our corpus, in order to show the performance of our corpus in classification tasks.