Ignacio Arroyo-Fernández


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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.

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UNAM at SemEval-2018 Task 10: Unsupervised Semantic Discriminative Attribute Identification in Neural Word Embedding Cones
Ignacio Arroyo-Fernández | Ivan Meza | Carlos-Francisco Méndez-Cruz
Proceedings of the 12th International Workshop on Semantic Evaluation

In this paper we report an unsupervised method aimed to identify whether an attribute is discriminative for two words (which are treated as concepts, in our particular case). To this end, we use geometrically inspired vector operations underlying unsupervised decision functions. These decision functions operate on state-of-the-art neural word embeddings of the attribute and the concepts. The main idea can be described as follows: if attribute q discriminates concept a from concept b, then q is excluded from the feature set shared by these two concepts: the intersection. That is, the membership q∈ (a∩ b) does not hold. As a,b,q are represented with neural word embeddings, we tested vector operations allowing us to measure membership, i.e. fuzzy set operations (t-norm, for fuzzy intersection, and t-conorm, for fuzzy union) and the similarity between q and the convex cone described by a and b.


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LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity
Ignacio Arroyo-Fernández | Ivan Vladimir Meza Ruiz
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper we report our attempt to use, on the one hand, state-of-the-art neural approaches that are proposed to measure Semantic Textual Similarity (STS). On the other hand, we propose an unsupervised cross-word alignment approach, which is linguistically motivated. The neural approaches proposed herein are divided into two main stages. The first stage deals with constructing neural word embeddings, the components of sentence embeddings. The second stage deals with constructing a semantic similarity function relating pairs of sentence embeddings. Unfortunately our competition results were poor in all tracks, therefore we concentrated our research to improve them for Track 5 (EN-EN).


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Learning Kernels for Semantic Clustering: A Deep Approach
Ignacio Arroyo-Fernández
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop