Emotion detection in text is a fundamental aspect of affective computing and is closely linked to natural language processing. Its applications span various domains, from interactive chatbots to marketing and customer service. This research specifically focuses on its significance in literature analysis and understanding. To facilitate this, we present a novel approach that involves creating a multi-label fine-grained emotion detection dataset, derived from literary sources. Our methodology employs a simple yet effective semi-supervised technique. We leverage textual entailment classification to perform emotion-specific weak-labeling, selecting examples with the highest and lowest scores from a large corpus. Utilizing these emotion-specific datasets, we train binary pseudo-labeling classifiers for each individual emotion. By applying this process to the selected examples, we construct a multi-label dataset. Using this dataset, we train models and evaluate their performance within a traditional supervised setting. Our model achieves an F1 score of 0.59 on our labeled gold set, showcasing its ability to effectively detect fine-grained emotions. Furthermore, we conduct evaluations of the model’s performance in zero- and few-shot transfer scenarios using benchmark datasets. Notably, our results indicate that the knowledge learned from our dataset exhibits transferability across diverse data domains, demonstrating its potential for broader applications beyond emotion detection in literature. Our contribution thus includes a multi-label fine-grained emotion detection dataset built from literature, the semi-supervised approach used to create it, as well as the models trained on it. This work provides a solid foundation for advancing emotion detection techniques and their utilization in various scenarios, especially within the cultural heritage analysis.

This article builds on work conducted and lessons learned within SILKNOW, a research project that aimed at enhancing the preservation and digital dissemination of silk heritage. Taking the project and this heritage typology as a case study in the digital transformation of cultural heritage institutions, it illustrates specific challenges that these institutions must face and demonstrates a few innovative answers to meet those challenges. The methodology combines approaches typical of the humanities and others usual in ICT, being inductive regarding materials and methods (consisting of a detailed review of existing online repositories and research projects devoted to textile heritage) and descriptive for the results and discussion (which explain at length the development of some tools and resources that responded to the needs detected in the previous analysis). The article reports on the state of the art and recent developments in the field of textile heritage, the tools implemented to allow the semantic access and text analysis of descriptive records associated with silk fabrics, and the spatiotemporal visualization of that information. Finally, it argues that institutional policies, namely the creation and free dissemination of open data related to cultural heritage are just as important as technical developments, showing why any future effort in these areas should take data sustainability, both in its technical and in institutional aspects, into account, since it is the most responsible and reasonable approach in terms of efficient resource allocation.

More than 7000 rare diseases affect over 400 million people, posing significant challenges for medical research and healthcare. The integration of precision medicine with artificial intelligence offers promising solutions. This work introduces a classifier developed to discern whether research and news articles pertain to rare or non-rare diseases. Our methodology involves extracting 709 rare disease MeSH terms from Mondo and MeSH to improve rare disease categorization. We evaluate our classifier on abstracts from PubMed/MEDLINE and an expert-annotated news dataset, which includes news articles on four selected rare neurodevelopmental disorders (NDDs)—considered the largest category of rare diseases—from a total of 16 analyzed. We achieved F1 scores of 85% for abstracts and 71% for news articles, demonstrating robustness across both datasets and highlighting the potential of integrating artificial intelligence and ontologies to improve disease classification. Although the results are promising, they also indicate the need for further refinement in managing data heterogeneity. Our classifier improves the identification and categorization of medical information, essential for advancing research, enhancing information access, influencing policy, and supporting personalized treatments. Future work will focus on expanding disease classification to distinguish between attributes such as infectious and hereditary diseases, addressing data heterogeneity, and incorporating multilingual capabilities.