Deep Learning and Natural Language Processing—3rd Edition

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 659

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Algoritmi Research Center, Informatics Department, University of Évora, 7002–554 Évora, Portugal
Interests: artificial intelligence; natural language processing
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Special Issue Information

Dear Colleagues,

Over the last years, natural language processing tasks have been able to improve their performance significantly through the use of deep learning methodologies. Machine translation, summarization, and question-answering systems are just some examples of tasks that science has been able to elevate to reach a high level of performance.

In this Special Issue, we welcome new research contributions and survey papers describing advances in this relevant domain. Potential topics include:

  • Deep learning architectures specialized for NLP;
  • Deep learning based approaches to NLP tasks, such as, NERC, syntactic parsers, semantic analysis, semantic role labeling, information extraction, sentiment analysis, summarization, question-answering and machine translation;
  • Hybrid (symbolic + deep learning) approaches to NLP;
  • Text annotation using deep-learning approaches;
  • Survey papers;
  • Generative models;
  • Large Language Models;
  • Knowledge Graphs and LLM.

Prof. Dr. Paulo Quaresma
Guest Editor

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Keywords

  • deep learning
  • natural language processing
  • NLP
  • generative AI
  • LLM
  • knowledge graphs and LLM

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Published Papers (1 paper)

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Research

32 pages, 465 KiB  
Article
EsCorpiusBias: The Contextual Annotation and Transformer-Based Detection of Racism and Sexism in Spanish Dialogue
by Ksenia Kharitonova, David Pérez-Fernández, Javier Gutiérrez-Hernando, Asier Gutiérrez-Fandiño, Zoraida Callejas and David Griol
Future Internet 2025, 17(8), 340; https://doi.org/10.3390/fi17080340 - 28 Jul 2025
Viewed by 261
Abstract
The rise in online communication platforms has significantly increased exposure to harmful discourse, presenting ongoing challenges for digital moderation and user well-being. This paper introduces the EsCorpiusBias corpus, designed to enhance the automated detection of sexism and racism within Spanish-language online dialogue, specifically [...] Read more.
The rise in online communication platforms has significantly increased exposure to harmful discourse, presenting ongoing challenges for digital moderation and user well-being. This paper introduces the EsCorpiusBias corpus, designed to enhance the automated detection of sexism and racism within Spanish-language online dialogue, specifically sourced from the Mediavida forum. By means of a systematic, context-sensitive annotation protocol, approximately 1000 three-turn dialogue units per bias category are annotated, ensuring the nuanced recognition of pragmatic and conversational subtleties. Here, annotation guidelines are meticulously developed, covering explicit and implicit manifestations of sexism and racism. Annotations are performed using the Prodigy tool (v1. 16.0) resulting in moderate to substantial inter-annotator agreement (Cohen’s Kappa: 0.55 for sexism and 0.79 for racism). Models including logistic regression, SpaCy’s baseline n-gram bag-of-words model, and transformer-based BETO are trained and evaluated, demonstrating that contextualized transformer-based approaches significantly outperform baseline and general-purpose models. Notably, the single-turn BETO model achieves an ROC-AUC of 0.94 for racism detection, while the contextual BETO model reaches an ROC-AUC of 0.87 for sexism detection, highlighting BETO’s superior effectiveness in capturing nuanced bias in online dialogues. Additionally, lexical overlap analyses indicate a strong reliance on explicit lexical indicators, highlighting limitations in handling implicit biases. This research underscores the importance of contextually grounded, domain-specific fine-tuning for effective automated detection of toxicity, providing robust resources and methodologies to foster socially responsible NLP systems within Spanish-speaking online communities. Full article
(This article belongs to the Special Issue Deep Learning and Natural Language Processing—3rd Edition)
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