Abstract
Over the last three years, the JOKER Lab series at CLEF has gathered an active community of researchers in natural language processing and information retrieval to collaborate on non-literal use of language in text. Such language can be a challenge for AI systems, but also sometimes for humans, as it requires understanding implicit cultural references and unorthodox interactions between form and meaning. In this paper, we discuss the lessons learned from the previous iterations of the Lab and describe how its upcoming edition will build upon those to address new challenges. In 2025, JOKER will provide novel tasks and update some previous ones with new data and new languages. This year we provide sandbox environments for experimenting with humour-aware information retrieval (Task 1), a previously featured task now enhanced with an all-new Portuguese corpus; wordplay translation in text (Task 2), another historical task for which we provide new corpora; onomastic wordplay (Task 3), a new task focussed on humorous proper names in fiction; and controlled creativity (Task 4), another novel task that aims at identifying and avoiding hallucinations.

Abstract
Around 80% of websites change significantly or disappear altogether after the first year, resulting in the loss of invaluable information. In this volatile scenario, preserving online content is increasingly essential. This is especially critical for local news outlets, which produce a wealth of information within the unique context of their communities but often lack sufficient archiving resources. In this paper, we take a significant step forward by leveraging the information preserved by the Portuguese Web Archive, Arquivo.pt, to recreate the website of a local news outlet. This online demo grants users direct access to previously lost news articles, images, and front covers, thus contributing to preserving local digital heritage. An IR system was also implemented to ensure easy access, along with a recommendation system based on BERT embeddings to suggest related news articles and enhance user engagement. As a final contribution, we also provide a Python package, enabling others to replicate the process of collecting, processing, retrieving, and recreating websites for local news outlets in Portugal.

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Abstract
Acquiring high-quality annotated data remains one of the most significant challenges in Natural Language Processing (NLP), especially for supervised learning approaches. In scenarios where pre-existing labeled data is unavailable, common solutions like crowdsourcing and zero-shot approaches often fall short, suffering from limitations such as the need for large datasets and a lack of guarantees regarding annotation quality. Traditionally, data for human annotation has been selected randomly, a practice that is not only costly and inefficient but also prone to bias, particularly in imbalanced datasets where minority classes are underrepresented. To address these challenges, this work introduces an automatic and informed data selection architecture designed to minimize the volume of required annotations while maximizing the diversity and representativeness of the selected data. Among the evaluated methods, Reverse Semantic Search (RSS) demonstrated superior performance, consistently outperforming random sampling in imbalanced scenarios and enhancing the effectiveness of trained classifiers. Furthermore, we compared RSS with other clustering-based approaches, providing insights into their respective strengths and weaknesses.

Abstract
The public transportation sector generates large volumes of sensor data that, if analyzed adequately, can help anticipate failures and initiate maintenance actions, thereby enhancing quality and productivity. This work contributes to a real-time data-driven predictive maintenance solution for Intelligent Transportation Systems. The proposed method implements a processing pipeline comprised of sample pre-processing, incremental classification with Machine Learning models, and outcome explanation. This novel online processing pipeline has two main highlights: (i) a dedicated sample pre-processing module, which builds statistical and frequency-related features on the fly, and (ii) an explainability module. This work is the first to perform online fault prediction with natural language and visual explainability. The experiments were performed with the Metropt data set from the metro operator of Porto, Portugal. The results are above 98 % for f-measure and 99 % for accuracy. In the context of railway predictive maintenance, achieving these high values is crucial due to the practical and operational implications of accurate failure prediction. In the specific case of a high f-measure, this ensures that the system maintains an optimal balance between detecting the highest possible number of real faults and minimizing false alarms, which is crucial for maximizing service availability. Furthermore, the accuracy obtained enables reliability, directly impacting cost reduction and increased safety. The analysis demonstrates that the pipeline maintains high performance even in the presence of class imbalance and noise, and its explanations effectively reflect the decision-making process. These findings validate the methodological soundness of the approach and confirm its practical applicability for supporting proactive maintenance decisions in real-world railway operations. Therefore, by identifying the early signs of failure, this pipeline enables decision-makers to understand the underlying problems and act accordingly swiftly.