HeteroCorpus: A Corpus for Heteronormative Language Detection

Vásquez, Juan Gabriel; Bel-Enguix, Gemma; Andersen, Scott W.; Ojeda-Trueba, Sergio-Luis

doi:10.18653/v1/2022.gebnlp-1.23

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Therefore, efforts have been made to compile multilingual corpora [42,43] as well as language-specific datasets such as Portuguese [44,45], Arabic [46,47], French [48], among others [49][50][51]. For the Spanish language, there are annotated tweet datasets for tasks such as hate speech detection [52], aggression detection [53], LGBT-phobia detection [54], and automatic stance detection [55], among others. However, to our knowledge, there is no manually annotated public corpus for the sentiment polarity of COVID-19-related tweets in Spanish.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

Gomez-Adorno,

Bel-Enguix,

Sierra

et al. 2024

Informatics

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This article presents a comprehensive evaluation of traditional machine learning and deep learning models in analyzing sentiment trends within the SENT-COVID Twitter corpus, curated during the COVID-19 pandemic. The corpus, filtered by COVID-19 related keywords and manually annotated for polarity, is a pivotal resource for conducting sentiment analysis experiments. Our study investigates various approaches, including classic vector-based systems such as word2vec, doc2vec, and diverse phrase modeling techniques, alongside Spanish pre-trained BERT models. We assess the performance of readily available sentiment analysis libraries for Python users, including TextBlob, VADER, and Pysentimiento. Additionally, we implement and evaluate traditional classification algorithms such as Logistic Regression, Naive Bayes, Support Vector Machines, and simple neural networks like Multilayer Perceptron. Throughout the research, we explore different dimensionality reduction techniques. This methodology enables a precise comparison among classification methods, with BETO-uncased achieving the highest accuracy of 0.73 on the test set. Our findings underscore the efficacy and applicability of traditional machine learning and deep learning models in analyzing sentiment trends within the context of low-resource Spanish language scenarios and emerging topics like COVID-19.

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Section: Related Workmentioning

confidence: 99%

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

Gomez-Adorno,

Bel-Enguix,

Sierra

et al. 2024

Informatics

View full text Add to dashboard Cite

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Bias and Fairness in Large Language Models: A Survey

Gallegos,

Rossi,

Barrow

et al. 2024

Computational Linguistics

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Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touch our social sphere. Despite this success, these models can learn, perpetuate, and amplify harmful social biases. In this article, we present a comprehensive survey of bias evaluation and mitigation techniques for LLMs. We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing, defining distinct facets of harm and introducing several desiderata to operationalize fairness for LLMs. We then unify the literature by proposing three intuitive taxonomies, two for bias evaluation, namely metrics and datasets, and one for mitigation. Our first taxonomy of metrics for bias evaluation disambiguates the relationship between metrics and evaluation datasets, and organizes metrics by the different levels at which they operate in a model: embeddings, probabilities, and generated text. Our second taxonomy of datasets for bias evaluation categorizes datasets by their structure as counterfactual inputs or prompts, and identifies the targeted harms and social groups; we also release a consolidation of publicly-available datasets for improved access. Our third taxonomy of techniques for bias mitigation classifies methods by their intervention during pre-processing, in-training, intra-processing, and post-processing, with granular subcategories that elucidate research trends. Finally, we identify open problems and challenges for future work. Synthesizing a wide range of recent research, we aim to provide a clear guide of the existing literature that empowers researchers and practitioners to better understand and prevent the propagation of bias in LLMs.

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HeteroCorpus: A Corpus for Heteronormative Language Detection

Cited by 2 publications

References 22 publications

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

Bias and Fairness in Large Language Models: A Survey

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