Hugo Jair Escalante


Self-Contextualized Attention for Abusive Language Identification
Horacio Jarquín-Vásquez | Hugo Jair Escalante | Manuel Montes
Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media

The use of attention mechanisms in deep learning approaches has become popular in natural language processing due to its outstanding performance. The use of these mechanisms allows one managing the importance of the elements of a sequence in accordance to their context, however, this importance has been observed independently between the pairs of elements of a sequence (self-attention) and between the application domain of a sequence (contextual attention), leading to the loss of relevant information and limiting the representation of the sequences. To tackle these particular issues we propose the self-contextualized attention mechanism, which trades off the previous limitations, by considering the internal and contextual relationships between the elements of a sequence. The proposed mechanism was evaluated in four standard collections for the abusive language identification task achieving encouraging results. It outperformed the current attention mechanisms and showed a competitive performance with respect to state-of-the-art approaches.


Early Text Classification Using Multi-Resolution Concept Representations
Adrian Pastor López-Monroy | Fabio A. González | Manuel Montes | Hugo Jair Escalante | Thamar Solorio
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

The intensive use of e-communications in everyday life has given rise to new threats and risks. When the vulnerable asset is the user, detecting these potential attacks before they cause serious damages is extremely important. This paper proposes a novel document representation to improve the early detection of risks in social media sources. The goal is to effectively identify the potential risk using as few text as possible and with as much anticipation as possible. Accordingly, we devise a Multi-Resolution Representation (MulR), which allows us to generate multiple “views” of the analyzed text. These views capture different semantic meanings for words and documents at different levels of detail, which is very useful in early scenarios to model the variable amounts of evidence. Intuitively, the representation captures better the content of short documents (very early stages) in low resolutions, whereas large documents (medium/large stages) are better modeled with higher resolutions. We evaluate the proposed ideas in two different tasks where anticipation is critical: sexual predator detection and depression detection. The experimental evaluation for these early tasks revealed that the proposed approach outperforms previous methodologies by a considerable margin.


Early text classification: a Naïve solution
Hugo Jair Escalante | Manuel Montes y Gomez | Luis Villasenor | Marcelo Luis Errecalde
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis


Sexual predator detection in chats with chained classifiers
Hugo Jair Escalante | Esaú Villatoro-Tello | Antonio Juárez | Manuel Montes-y-Gómez | Luis Villaseñor
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis


Local Histograms of Character N-grams for Authorship Attribution
Hugo Jair Escalante | Thamar Solorio | Manuel Montes-y-Gómez
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies