Ricardo Muñoz Sánchez


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Scaling-up the Resources for a Freely Available Swedish VADER (svVADER)
Dimitrios Kokkinakis | Ricardo Muñoz Sánchez | Mia-Marie Hammarlin
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

With widespread commercial applications in various domains, sentiment analysis has become a success story for Natural Language Processing (NLP). Still, although sentiment analysis has rapidly progressed during the last years, mainly due to the application of modern AI technologies, many approaches apply knowledge-based strategies, such as lexicon-based, to the task. This is particularly true for analyzing short social media content, e.g., tweets. Moreover, lexicon-based sentiment analysis approaches are usually preferred over learning-based methods when training data is unavailable or insufficient. Therefore, our main goal is to scale-up and apply a lexicon-based approach which can be used as a baseline to Swedish sentiment analysis. All scaled-up resources are made available, while the performance of this enhanced tool is evaluated on two short datasets, achieving adequate results.

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Investigating the Effects of MWE Identification in Structural Topic Modelling
Dimitrios Kokkinakis | Ricardo Muñoz Sánchez | Sebastianus Bruinsma | Mia-Marie Hammarlin
Proceedings of the 19th Workshop on Multiword Expressions (MWE 2023)

Multiword expressions (MWEs) are common word combinations which exhibit idiosyncrasies in various linguistic levels. For various downstream natural language processing applications and tasks, the identification and discovery of MWEs has been proven to be potentially practical and useful, but still challenging to codify. In this paper we investigate various, relevant to MWE, resources and tools for Swedish, and, within a specific application scenario, namely ‘vaccine skepticism’, we apply structural topic modelling to investigate whether there are any interpretative advantages of identifying MWEs.


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A First Attempt at Unreliable News Detection in Swedish
Ricardo Muñoz Sánchez | Eric Johansson | Shakila Tayefeh | Shreyash Kad
Proceedings of the Second International Workshop on Resources and Techniques for User Information in Abusive Language Analysis

Throughout the COVID-19 pandemic, a parallel infodemic has also been going on such that the information has been spreading faster than the virus itself. During this time, every individual needs to access accurate news in order to take corresponding protective measures, regardless of their country of origin or the language they speak, as misinformation can cause significant loss to not only individuals but also society. In this paper we train several machine learning models (ranging from traditional machine learning to deep learning) to try to determine whether news articles come from either a reliable or an unreliable source, using just the body of the article. Moreover, we use a previously introduced corpus of news in Swedish related to the COVID-19 pandemic for the classification task. Given that our dataset is both unbalanced and small, we use subsampling and easy data augmentation (EDA) to try to solve these issues. In the end, we realize that, due to the small size of our dataset, using traditional machine learning along with data augmentation yields results that rival those of transformer models such as BERT.


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Intrinsic Bias Metrics Do Not Correlate with Application Bias
Seraphina Goldfarb-Tarrant | Rebecca Marchant | Ricardo Muñoz Sánchez | Mugdha Pandya | Adam Lopez
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Natural Language Processing (NLP) systems learn harmful societal biases that cause them to amplify inequality as they are deployed in more and more situations. To guide efforts at debiasing these systems, the NLP community relies on a variety of metrics that quantify bias in models. Some of these metrics are intrinsic, measuring bias in word embedding spaces, and some are extrinsic, measuring bias in downstream tasks that the word embeddings enable. Do these intrinsic and extrinsic metrics correlate with each other? We compare intrinsic and extrinsic metrics across hundreds of trained models covering different tasks and experimental conditions. Our results show no reliable correlation between these metrics that holds in all scenarios across tasks and languages. We urge researchers working on debiasing to focus on extrinsic measures of bias, and to make using these measures more feasible via creation of new challenge sets and annotated test data. To aid this effort, we release code, a new intrinsic metric, and an annotated test set focused on gender bias in hate speech.